[tvm-site] branch asf-site updated: deploying docs (apache/tvm@7d9b7bbd503dc4365f803541f56edf1e34020925)

[tq...@apache.org]

This is an automated email from the ASF dual-hosted git repository.

tqchen pushed a commit to branch asf-site
in repository https://gitbox.apache.org/repos/asf/tvm-site.git


The following commit(s) were added to refs/heads/asf-site by this push:
     new 0dd0a907c deploying docs (apache/tvm@7d9b7bbd503dc4365f803541f56edf1e34020925)
0dd0a907c is described below

commit 0dd0a907c24255600406102b2c8442e2755b648b
Author: tvm-bot <95...@users.noreply.github.com>
AuthorDate: Sun Apr 17 05:10:42 2022 +0000

    deploying docs (apache/tvm@7d9b7bbd503dc4365f803541f56edf1e34020925)
---
 .../how_to/compile_models/from_mxnet.rst.txt       |    2 +-
 .../how_to/compile_models/from_paddle.rst.txt      |    2 +-
 .../how_to/compile_models/from_pytorch.rst.txt     |    2 +-
 .../how_to/compile_models/from_tensorflow.rst.txt  |    5 +
 .../compile_models/sg_execution_times.rst.txt      |   20 +-
 .../deploy_models/deploy_model_on_android.rst.txt  |    2 +-
 .../deploy_object_detection_pytorch.rst.txt        |    4 +-
 .../deploy_models/deploy_prequantized.rst.txt      |    6 +-
 .../deploy_prequantized_tflite.rst.txt             |    4 +-
 .../how_to/deploy_models/deploy_quantized.rst.txt  |    2 +-
 .../deploy_models/deploy_ssd_gluoncv.rst.txt       |    4 +-
 .../deploy_models/sg_execution_times.rst.txt       |   18 +-
 .../extend_tvm/bring_your_own_datatypes.rst.txt    |    2 +-
 .../how_to/extend_tvm/sg_execution_times.rst.txt   |   10 +-
 .../how_to/extend_tvm/use_pass_instrument.rst.txt  |   16 +-
 .../optimize_operators/opt_conv_cuda.rst.txt       |    2 +-
 .../optimize_operators/opt_conv_tensorcore.rst.txt |    2 +-
 .../how_to/optimize_operators/opt_gemm.rst.txt     |   16 +-
 .../optimize_operators/sg_execution_times.rst.txt  |    8 +-
 .../sg_execution_times.rst.txt                     |   16 +-
 .../tune_conv2d_layer_cuda.rst.txt                 | 1558 ++++++++++++--------
 .../tune_network_cuda.rst.txt                      |    2 +-
 .../tune_network_x86.rst.txt                       |    4 +-
 .../tune_sparse_x86.rst.txt                        |  464 +-----
 .../tune_with_autotvm/sg_execution_times.rst.txt   |   12 +-
 .../tune_with_autotvm/tune_conv2d_cuda.rst.txt     |   34 +-
 .../work_with_microtvm/micro_autotune.rst.txt      |   16 +-
 .../work_with_microtvm/sg_execution_times.rst.txt  |   12 +-
 .../work_with_relay/sg_execution_times.rst.txt     |    8 +-
 .../work_with_schedules/sg_execution_times.rst.txt |   18 +-
 .../how_to/work_with_schedules/tensorize.rst.txt   |    2 +-
 .../tutorials/autotvm/sg_execution_times.rst.txt   |    6 +-
 .../frontend/deploy_classification.rst.txt         |    2 +-
 .../tutorials/frontend/deploy_detection.rst.txt    |    2 +-
 .../tutorials/frontend/sg_execution_times.rst.txt  |    6 +-
 .../tutorials/optimize/sg_execution_times.rst.txt  |    6 +-
 .../topic/vta/tutorials/sg_execution_times.rst.txt |    6 +-
 .../tutorial/auto_scheduler_matmul_x86.rst.txt     |    9 +-
 docs/_sources/tutorial/autotvm_relay_x86.rst.txt   |   58 +-
 .../tutorial/cross_compilation_and_rpc.rst.txt     |    2 +-
 docs/_sources/tutorial/intro_topi.rst.txt          |    2 +-
 docs/_sources/tutorial/sg_execution_times.rst.txt  |   26 +-
 .../tutorial/tensor_expr_get_started.rst.txt       |   44 +-
 docs/commit_hash                                   |    2 +-
 docs/how_to/compile_models/from_mxnet.html         |    2 +-
 docs/how_to/compile_models/from_paddle.html        |    2 +-
 docs/how_to/compile_models/from_pytorch.html       |    6 +-
 docs/how_to/compile_models/from_tensorflow.html    |    1 +
 docs/how_to/compile_models/sg_execution_times.html |   20 +-
 .../deploy_models/deploy_model_on_android.html     |    2 +-
 .../deploy_object_detection_pytorch.html           |   26 +-
 docs/how_to/deploy_models/deploy_prequantized.html |    6 +-
 .../deploy_models/deploy_prequantized_tflite.html  |    4 +-
 docs/how_to/deploy_models/deploy_quantized.html    |    2 +-
 docs/how_to/deploy_models/deploy_ssd_gluoncv.html  |   41 +-
 docs/how_to/deploy_models/sg_execution_times.html  |   18 +-
 .../extend_tvm/bring_your_own_datatypes.html       |    2 +-
 docs/how_to/extend_tvm/sg_execution_times.html     |   10 +-
 docs/how_to/extend_tvm/use_pass_instrument.html    |   16 +-
 docs/how_to/optimize_operators/opt_conv_cuda.html  |    2 +-
 .../optimize_operators/opt_conv_tensorcore.html    |    2 +-
 docs/how_to/optimize_operators/opt_gemm.html       |   16 +-
 .../optimize_operators/sg_execution_times.html     |    8 +-
 .../sg_execution_times.html                        |   14 +-
 .../tune_conv2d_layer_cuda.html                    | 1558 ++++++++++++--------
 .../tune_with_autoscheduler/tune_network_cuda.html |    2 +-
 .../tune_with_autoscheduler/tune_network_x86.html  |    4 +-
 .../tune_with_autoscheduler/tune_sparse_x86.html   |  464 +-----
 .../tune_with_autotvm/sg_execution_times.html      |   12 +-
 .../how_to/tune_with_autotvm/tune_conv2d_cuda.html |   34 +-
 docs/how_to/work_with_microtvm/micro_autotune.html |   16 +-
 .../work_with_microtvm/sg_execution_times.html     |   12 +-
 .../how_to/work_with_relay/sg_execution_times.html |    8 +-
 .../work_with_schedules/sg_execution_times.html    |   18 +-
 docs/how_to/work_with_schedules/tensorize.html     |    2 +-
 docs/reference/api/python/auto_scheduler.html      |    4 +-
 .../api/typedoc/classes/bytestreamreader.html      |   12 +-
 .../api/typedoc/classes/cachedcallstack.html       |   34 +-
 docs/reference/api/typedoc/classes/dldatatype.html |   12 +-
 docs/reference/api/typedoc/classes/dldevice.html   |   10 +-
 .../reference/api/typedoc/classes/environment.html |   12 +-
 docs/reference/api/typedoc/classes/ffilibrary.html |   20 +-
 .../api/typedoc/classes/graphexecutor.html         |   16 +-
 docs/reference/api/typedoc/classes/instance.html   |   40 +-
 docs/reference/api/typedoc/classes/memory.html     |   34 +-
 docs/reference/api/typedoc/classes/module.html     |   10 +-
 docs/reference/api/typedoc/classes/ndarray.html    |   22 +-
 .../api/typedoc/classes/packedfunccell.html        |    6 +-
 docs/reference/api/typedoc/classes/rpcserver.html  |   14 +-
 docs/reference/api/typedoc/classes/scalar.html     |    6 +-
 .../api/typedoc/classes/webgpucontext.html         |   12 +-
 docs/reference/api/typedoc/enums/argtypecode.html  |   30 +-
 .../api/typedoc/enums/aynccallbackcode.html        |    4 +-
 .../api/typedoc/enums/dldatatypecode.html          |    8 +-
 .../api/typedoc/enums/rpcserverstate.html          |   12 +-
 docs/reference/api/typedoc/enums/sizeof.html       |   18 +-
 docs/reference/api/typedoc/index.html              |  112 +-
 .../api/typedoc/interfaces/disposable.html         |    2 +-
 .../api/typedoc/interfaces/functioninfo.html       |    6 +-
 .../api/typedoc/interfaces/libraryprovider.html    |    4 +-
 docs/searchindex.js                                |    2 +-
 .../vta/tutorials/autotvm/sg_execution_times.html  |    6 +-
 .../tutorials/frontend/deploy_classification.html  |    2 +-
 .../vta/tutorials/frontend/deploy_detection.html   |    2 +-
 .../vta/tutorials/frontend/sg_execution_times.html |    6 +-
 .../vta/tutorials/optimize/sg_execution_times.html |    6 +-
 docs/topic/vta/tutorials/sg_execution_times.html   |    6 +-
 docs/tutorial/auto_scheduler_matmul_x86.html       |    5 +-
 docs/tutorial/autotvm_relay_x86.html               |  166 +--
 docs/tutorial/cross_compilation_and_rpc.html       |    2 +-
 docs/tutorial/intro_topi.html                      |    2 +-
 docs/tutorial/sg_execution_times.html              |   26 +-
 docs/tutorial/tensor_expr_get_started.html         |   44 +-
 113 files changed, 2704 insertions(+), 2805 deletions(-)

diff --git a/docs/_sources/how_to/compile_models/from_mxnet.rst.txt b/docs/_sources/how_to/compile_models/from_mxnet.rst.txt
index b33aec6fc..2eb0c6638 100644
--- a/docs/_sources/how_to/compile_models/from_mxnet.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_mxnet.rst.txt
@@ -98,7 +98,7 @@ In this section, we download a pretrained imagenet model and classify an image.
 
  .. code-block:: none
 
-    Downloading /workspace/.mxnet/models/resnet18_v1-a0666292.zip6d3e2761-0eae-4f2a-aa04-e3b1d5eca3da from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/resnet18_v1-a0666292.zip...
+    Downloading /workspace/.mxnet/models/resnet18_v1-a0666292.zip1c1becf4-3740-4016-b91f-8ea51d5c1905 from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/resnet18_v1-a0666292.zip...
     x (1, 3, 224, 224)
 
 
diff --git a/docs/_sources/how_to/compile_models/from_paddle.rst.txt b/docs/_sources/how_to/compile_models/from_paddle.rst.txt
index ed33ca0ac..037c47913 100644
--- a/docs/_sources/how_to/compile_models/from_paddle.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_paddle.rst.txt
@@ -201,7 +201,7 @@ Look up prediction top 1 index in 1000 class synset.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 1 minutes  3.900 seconds)
+   **Total running time of the script:** ( 1 minutes  6.727 seconds)
 
 
 .. _sphx_glr_download_how_to_compile_models_from_paddle.py:
diff --git a/docs/_sources/how_to/compile_models/from_pytorch.rst.txt b/docs/_sources/how_to/compile_models/from_pytorch.rst.txt
index 1f81d9bd7..98633b43c 100644
--- a/docs/_sources/how_to/compile_models/from_pytorch.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_pytorch.rst.txt
@@ -79,7 +79,7 @@ Load a pretrained PyTorch model
  .. code-block:: none
 
     Downloading: "https://download.pytorch.org/models/resnet18-f37072fd.pth" to /workspace/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth
-
      0%|          | 0.00/44.7M [00:00<?, ?B/s]
      7%|6         | 3.05M/44.7M [00:00<00:01, 32.0MB/s]
     17%|#7        | 7.61M/44.7M [00:00<00:00, 41.0MB/s]
     69%|######8   | 30.7M/44.7M [00:00<00:00, 133MB/s] 
    100%|##########| 44.7M/44.7M [00:00<00:00, 131MB/s]
+
      0%|          | 0.00/44.7M [00:00<?, ?B/s]
     43%|####3     | 19.2M/44.7M [00:00<00:00, 201MB/s]
    100%|##########| 44.7M/44.7M [00:00<00:00, 237MB/s]
 
 
 
diff --git a/docs/_sources/how_to/compile_models/from_tensorflow.rst.txt b/docs/_sources/how_to/compile_models/from_tensorflow.rst.txt
index 43d9b260b..bfed34c91 100644
--- a/docs/_sources/how_to/compile_models/from_tensorflow.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_tensorflow.rst.txt
@@ -370,6 +370,11 @@ Run the corresponding model on tensorflow
 
 
 
+.. rst-class:: sphx-glr-timing
+
+   **Total running time of the script:** ( 1 minutes  4.689 seconds)
+
+
 .. _sphx_glr_download_how_to_compile_models_from_tensorflow.py:
 
 
diff --git a/docs/_sources/how_to/compile_models/sg_execution_times.rst.txt b/docs/_sources/how_to/compile_models/sg_execution_times.rst.txt
index 93a7a29b4..f1e32c833 100644
--- a/docs/_sources/how_to/compile_models/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/compile_models/sg_execution_times.rst.txt
@@ -5,14 +5,14 @@
 
 Computation times
 =================
-**04:38.468** total execution time for **how_to_compile_models** files:
+**05:00.096** total execution time for **how_to_compile_models** files:
 
-- **01:03.900**: :ref:`sphx_glr_how_to_compile_models_from_paddle.py` (``from_paddle.py``)
-- **00:59.085**: :ref:`sphx_glr_how_to_compile_models_from_tensorflow.py` (``from_tensorflow.py``)
-- **00:55.277**: :ref:`sphx_glr_how_to_compile_models_from_darknet.py` (``from_darknet.py``)
-- **00:25.181**: :ref:`sphx_glr_how_to_compile_models_from_tflite.py` (``from_tflite.py``)
-- **00:20.886**: :ref:`sphx_glr_how_to_compile_models_from_coreml.py` (``from_coreml.py``)
-- **00:20.807**: :ref:`sphx_glr_how_to_compile_models_from_mxnet.py` (``from_mxnet.py``)
-- **00:18.207**: :ref:`sphx_glr_how_to_compile_models_from_pytorch.py` (``from_pytorch.py``)
-- **00:12.435**: :ref:`sphx_glr_how_to_compile_models_from_keras.py` (``from_keras.py``)
-- **00:02.691**: :ref:`sphx_glr_how_to_compile_models_from_onnx.py` (``from_onnx.py``)
+- **01:06.727**: :ref:`sphx_glr_how_to_compile_models_from_paddle.py` (``from_paddle.py``)
+- **01:04.689**: :ref:`sphx_glr_how_to_compile_models_from_tensorflow.py` (``from_tensorflow.py``)
+- **00:59.126**: :ref:`sphx_glr_how_to_compile_models_from_darknet.py` (``from_darknet.py``)
+- **00:25.745**: :ref:`sphx_glr_how_to_compile_models_from_tflite.py` (``from_tflite.py``)
+- **00:25.278**: :ref:`sphx_glr_how_to_compile_models_from_coreml.py` (``from_coreml.py``)
+- **00:21.885**: :ref:`sphx_glr_how_to_compile_models_from_mxnet.py` (``from_mxnet.py``)
+- **00:19.756**: :ref:`sphx_glr_how_to_compile_models_from_pytorch.py` (``from_pytorch.py``)
+- **00:14.141**: :ref:`sphx_glr_how_to_compile_models_from_keras.py` (``from_keras.py``)
+- **00:02.749**: :ref:`sphx_glr_how_to_compile_models_from_onnx.py` (``from_onnx.py``)
diff --git a/docs/_sources/how_to/deploy_models/deploy_model_on_android.rst.txt b/docs/_sources/how_to/deploy_models/deploy_model_on_android.rst.txt
index dff75d791..075f6ec25 100644
--- a/docs/_sources/how_to/deploy_models/deploy_model_on_android.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_model_on_android.rst.txt
@@ -393,7 +393,7 @@ Execute on TVM
     Evaluate inference time cost...
     Execution time summary:
      mean (ms)   median (ms)    max (ms)     min (ms)     std (ms)  
-      15.9951      15.9650      16.4304      15.8805       0.1509   
+      16.4276      16.4296      16.5274      16.3232       0.0568   
                
 
 
diff --git a/docs/_sources/how_to/deploy_models/deploy_object_detection_pytorch.rst.txt b/docs/_sources/how_to/deploy_models/deploy_object_detection_pytorch.rst.txt
index 8635eca43..9ceae230e 100644
--- a/docs/_sources/how_to/deploy_models/deploy_object_detection_pytorch.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_object_detection_pytorch.rst.txt
@@ -108,7 +108,7 @@ Load pre-trained maskrcnn from torchvision and do tracing
  .. code-block:: none
 
     Downloading: "https://download.pytorch.org/models/maskrcnn_resnet50_fpn_coco-bf2d0c1e.pth" to /workspace/.cache/torch/hub/checkpoints/maskrcnn_resnet50_fpn_coco-bf2d0c1e.pth
-
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+
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     /usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:3878: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
       for i in range(dim)
     /usr/local/lib/python3.7/dist-packages/torchvision/models/detection/anchor_utils.py:127: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').
@@ -253,7 +253,7 @@ Get boxes with score larger than 0.9
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 3 minutes  0.840 seconds)
+   **Total running time of the script:** ( 3 minutes  18.369 seconds)
 
 
 .. _sphx_glr_download_how_to_deploy_models_deploy_object_detection_pytorch.py:
diff --git a/docs/_sources/how_to/deploy_models/deploy_prequantized.rst.txt b/docs/_sources/how_to/deploy_models/deploy_prequantized.rst.txt
index 0e5e74bab..998f45d5b 100644
--- a/docs/_sources/how_to/deploy_models/deploy_prequantized.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_prequantized.rst.txt
@@ -187,7 +187,7 @@ training. Other models require a full post training calibration.
  .. code-block:: none
 
     Downloading: "https://download.pytorch.org/models/mobilenet_v2-b0353104.pth" to /workspace/.cache/torch/hub/checkpoints/mobilenet_v2-b0353104.pth
-
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    100%|##########| 13.6M/13.6M [00:00<00:00, 185MB/s]
+
      0%|          | 0.00/13.6M [00:00<?, ?B/s]
    100%|##########| 13.6M/13.6M [00:00<00:00, 181MB/s]
 
 
 
@@ -344,7 +344,7 @@ Here we give an example of how to measure performance of TVM compiled models.
 
     Execution time summary:
      mean (ms)   median (ms)    max (ms)     min (ms)     std (ms)  
-      90.1193      90.0943      91.0812      89.8953       0.1627   
+      90.7121      90.4435      97.5283      90.3172       1.1237   
                
 
 
@@ -384,7 +384,7 @@ TODO
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 1 minutes  4.586 seconds)
+   **Total running time of the script:** ( 1 minutes  8.361 seconds)
 
 
 .. _sphx_glr_download_how_to_deploy_models_deploy_prequantized.py:
diff --git a/docs/_sources/how_to/deploy_models/deploy_prequantized_tflite.rst.txt b/docs/_sources/how_to/deploy_models/deploy_prequantized_tflite.rst.txt
index a5f3881ad..b5490e6f9 100644
--- a/docs/_sources/how_to/deploy_models/deploy_prequantized_tflite.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_prequantized_tflite.rst.txt
@@ -351,7 +351,7 @@ Here we give an example of how to measure performance of TVM compiled models.
 
     Execution time summary:
      mean (ms)   median (ms)    max (ms)     min (ms)     std (ms)  
-      119.4266     119.4160     120.6976     118.3389      0.4110   
+      122.1462     122.1530     122.7372     121.4437      0.2733   
                
 
 
@@ -385,7 +385,7 @@ Here we give an example of how to measure performance of TVM compiled models.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 1 minutes  54.100 seconds)
+   **Total running time of the script:** ( 1 minutes  54.368 seconds)
 
 
 .. _sphx_glr_download_how_to_deploy_models_deploy_prequantized_tflite.py:
diff --git a/docs/_sources/how_to/deploy_models/deploy_quantized.rst.txt b/docs/_sources/how_to/deploy_models/deploy_quantized.rst.txt
index 596872e48..c7214d30d 100644
--- a/docs/_sources/how_to/deploy_models/deploy_quantized.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_quantized.rst.txt
@@ -221,7 +221,7 @@ We create a Relay VM to build and execute the model.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 1 minutes  9.426 seconds)
+   **Total running time of the script:** ( 1 minutes  16.170 seconds)
 
 
 .. _sphx_glr_download_how_to_deploy_models_deploy_quantized.py:
diff --git a/docs/_sources/how_to/deploy_models/deploy_ssd_gluoncv.rst.txt b/docs/_sources/how_to/deploy_models/deploy_ssd_gluoncv.rst.txt
index 4baebef85..a08e08484 100644
--- a/docs/_sources/how_to/deploy_models/deploy_ssd_gluoncv.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_ssd_gluoncv.rst.txt
@@ -137,7 +137,7 @@ Convert and compile model for CPU.
             data: None
       input_sym_arg_type = in_param.infer_type()[0]
     Downloading /workspace/.mxnet/models/ssd_512_resnet50_v1_voc-9c8b225a.zip from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/ssd_512_resnet50_v1_voc-9c8b225a.zip...
-
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+
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@@ -202,7 +202,7 @@ Display result
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 2 minutes  21.536 seconds)
+   **Total running time of the script:** ( 2 minutes  31.083 seconds)
 
 
 .. _sphx_glr_download_how_to_deploy_models_deploy_ssd_gluoncv.py:
diff --git a/docs/_sources/how_to/deploy_models/sg_execution_times.rst.txt b/docs/_sources/how_to/deploy_models/sg_execution_times.rst.txt
index ac0e79099..424b6fd19 100644
--- a/docs/_sources/how_to/deploy_models/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/deploy_models/sg_execution_times.rst.txt
@@ -5,13 +5,13 @@
 
 Computation times
 =================
-**10:19.507** total execution time for **how_to_deploy_models** files:
+**11:00.202** total execution time for **how_to_deploy_models** files:
 
-- **03:00.840**: :ref:`sphx_glr_how_to_deploy_models_deploy_object_detection_pytorch.py` (``deploy_object_detection_pytorch.py``)
-- **02:21.536**: :ref:`sphx_glr_how_to_deploy_models_deploy_ssd_gluoncv.py` (``deploy_ssd_gluoncv.py``)
-- **01:54.100**: :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized_tflite.py` (``deploy_prequantized_tflite.py``)
-- **01:09.426**: :ref:`sphx_glr_how_to_deploy_models_deploy_quantized.py` (``deploy_quantized.py``)
-- **01:04.586**: :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized.py` (``deploy_prequantized.py``)
-- **00:27.722**: :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_android.py` (``deploy_model_on_android.py``)
-- **00:21.110**: :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_rasp.py` (``deploy_model_on_rasp.py``)
-- **00:00.188**: :ref:`sphx_glr_how_to_deploy_models_deploy_sparse.py` (``deploy_sparse.py``)
+- **03:18.369**: :ref:`sphx_glr_how_to_deploy_models_deploy_object_detection_pytorch.py` (``deploy_object_detection_pytorch.py``)
+- **02:31.083**: :ref:`sphx_glr_how_to_deploy_models_deploy_ssd_gluoncv.py` (``deploy_ssd_gluoncv.py``)
+- **01:54.368**: :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized_tflite.py` (``deploy_prequantized_tflite.py``)
+- **01:16.170**: :ref:`sphx_glr_how_to_deploy_models_deploy_quantized.py` (``deploy_quantized.py``)
+- **01:08.361**: :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized.py` (``deploy_prequantized.py``)
+- **00:29.370**: :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_android.py` (``deploy_model_on_android.py``)
+- **00:22.282**: :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_rasp.py` (``deploy_model_on_rasp.py``)
+- **00:00.197**: :ref:`sphx_glr_how_to_deploy_models_deploy_sparse.py` (``deploy_sparse.py``)
diff --git a/docs/_sources/how_to/extend_tvm/bring_your_own_datatypes.rst.txt b/docs/_sources/how_to/extend_tvm/bring_your_own_datatypes.rst.txt
index a7463d024..dc1735b59 100644
--- a/docs/_sources/how_to/extend_tvm/bring_your_own_datatypes.rst.txt
+++ b/docs/_sources/how_to/extend_tvm/bring_your_own_datatypes.rst.txt
@@ -423,7 +423,7 @@ First let us define two helper functions to get the mobilenet model and a cat im
 
  .. code-block:: none
 
-    Downloading /workspace/.mxnet/models/mobilenet0.25-9f83e440.zip1c0edb4c-2721-4485-a82f-207087b6065e from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/mobilenet0.25-9f83e440.zip...
+    Downloading /workspace/.mxnet/models/mobilenet0.25-9f83e440.zip89da06ac-6dbc-4b53-a94a-d688ee318fc2 from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/mobilenet0.25-9f83e440.zip...
 
 
 
diff --git a/docs/_sources/how_to/extend_tvm/sg_execution_times.rst.txt b/docs/_sources/how_to/extend_tvm/sg_execution_times.rst.txt
index 123d8f362..9fe71748a 100644
--- a/docs/_sources/how_to/extend_tvm/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/extend_tvm/sg_execution_times.rst.txt
@@ -5,9 +5,9 @@
 
 Computation times
 =================
-**00:37.772** total execution time for **how_to_extend_tvm** files:
+**00:39.897** total execution time for **how_to_extend_tvm** files:
 
-- **00:34.314**: :ref:`sphx_glr_how_to_extend_tvm_bring_your_own_datatypes.py` (``bring_your_own_datatypes.py``)
-- **00:02.210**: :ref:`sphx_glr_how_to_extend_tvm_use_pass_instrument.py` (``use_pass_instrument.py``)
-- **00:01.048**: :ref:`sphx_glr_how_to_extend_tvm_use_pass_infra.py` (``use_pass_infra.py``)
-- **00:00.200**: :ref:`sphx_glr_how_to_extend_tvm_low_level_custom_pass.py` (``low_level_custom_pass.py``)
+- **00:36.255**: :ref:`sphx_glr_how_to_extend_tvm_bring_your_own_datatypes.py` (``bring_your_own_datatypes.py``)
+- **00:02.342**: :ref:`sphx_glr_how_to_extend_tvm_use_pass_instrument.py` (``use_pass_instrument.py``)
+- **00:01.093**: :ref:`sphx_glr_how_to_extend_tvm_use_pass_infra.py` (``use_pass_infra.py``)
+- **00:00.207**: :ref:`sphx_glr_how_to_extend_tvm_low_level_custom_pass.py` (``low_level_custom_pass.py``)
diff --git a/docs/_sources/how_to/extend_tvm/use_pass_instrument.rst.txt b/docs/_sources/how_to/extend_tvm/use_pass_instrument.rst.txt
index cc6b5bdc2..bf7db616a 100644
--- a/docs/_sources/how_to/extend_tvm/use_pass_instrument.rst.txt
+++ b/docs/_sources/how_to/extend_tvm/use_pass_instrument.rst.txt
@@ -199,10 +199,10 @@ profile the execution time of each passes.
  .. code-block:: none
 
     Printing results of timing profile...
-    InferType: 5825us [5825us] (44.98%; 44.98%)
-    FoldScaleAxis: 7124us [2us] (55.02%; 55.02%)
-            FoldConstant: 7122us [1502us] (55.00%; 99.97%)
-                    InferType: 5620us [5620us] (43.40%; 78.92%)
+    InferType: 5965us [5965us] (45.27%; 45.27%)
+    FoldScaleAxis: 7211us [2us] (54.73%; 54.73%)
+            FoldConstant: 7209us [1463us] (54.71%; 99.97%)
+                    InferType: 5745us [5745us] (43.60%; 79.70%)
 
 
 
@@ -239,10 +239,10 @@ Refer to following sections and :py:func:`tvm.instrument.pass_instrument` for th
  .. code-block:: none
 
     Printing results of timing profile...
-    InferType: 5729us [5729us] (44.57%; 44.57%)
-    FoldScaleAxis: 7124us [2us] (55.43%; 55.43%)
-            FoldConstant: 7122us [1488us] (55.41%; 99.98%)
-                    InferType: 5634us [5634us] (43.83%; 79.10%)
+    InferType: 5874us [5874us] (44.70%; 44.70%)
+    FoldScaleAxis: 7267us [2us] (55.30%; 55.30%)
+            FoldConstant: 7265us [1490us] (55.28%; 99.97%)
+                    InferType: 5774us [5774us] (43.94%; 79.48%)
 
 
 
diff --git a/docs/_sources/how_to/optimize_operators/opt_conv_cuda.rst.txt b/docs/_sources/how_to/optimize_operators/opt_conv_cuda.rst.txt
index 34f06eceb..1e38c893a 100644
--- a/docs/_sources/how_to/optimize_operators/opt_conv_cuda.rst.txt
+++ b/docs/_sources/how_to/optimize_operators/opt_conv_cuda.rst.txt
@@ -295,7 +295,7 @@ latency of convolution.
 
  .. code-block:: none
 
-    Convolution: 44.464928 ms
+    Convolution: 45.031039 ms
 
 
 
diff --git a/docs/_sources/how_to/optimize_operators/opt_conv_tensorcore.rst.txt b/docs/_sources/how_to/optimize_operators/opt_conv_tensorcore.rst.txt
index 383641187..889849f77 100644
--- a/docs/_sources/how_to/optimize_operators/opt_conv_tensorcore.rst.txt
+++ b/docs/_sources/how_to/optimize_operators/opt_conv_tensorcore.rst.txt
@@ -626,7 +626,7 @@ be able to run on our build server
 
  .. code-block:: none
 
-    conv2d with tensor core: 6.923151 ms
+    conv2d with tensor core: 10.467079 ms
 
 
 
diff --git a/docs/_sources/how_to/optimize_operators/opt_gemm.rst.txt b/docs/_sources/how_to/optimize_operators/opt_gemm.rst.txt
index 9fb039ad2..51cd12bf5 100644
--- a/docs/_sources/how_to/optimize_operators/opt_gemm.rst.txt
+++ b/docs/_sources/how_to/optimize_operators/opt_gemm.rst.txt
@@ -118,8 +118,8 @@ Then we write a baseline implementation, the simplest way to write a matrix mult
 
  .. code-block:: none
 
-    Numpy running time: 0.018355
-    Baseline: 3.388145
+    Numpy running time: 0.019652
+    Baseline: 3.352839
 
 
 
@@ -209,7 +209,7 @@ fill 32 * 32 * sizeof(float) which is 4KB in the cache whose total size is 32KB
 
  .. code-block:: none
 
-    Opt1: 0.304776
+    Opt1: 0.320437
 
 
 
@@ -307,7 +307,7 @@ In this tutorial, we chose to vectorize the inner loop row data since it is cach
 
  .. code-block:: none
 
-    Opt2: 0.339592
+    Opt2: 0.341972
 
 
 
@@ -398,7 +398,7 @@ the access pattern for A matrix is more cache friendly.
 
  .. code-block:: none
 
-    Opt3: 0.116929
+    Opt3: 0.134355
 
 
 
@@ -516,7 +516,7 @@ flattening.
 
  .. code-block:: none
 
-    Opt4: 0.110666
+    Opt4: 0.112803
 
 
 
@@ -633,7 +633,7 @@ write to C when all the block results are ready.
 
  .. code-block:: none
 
-    Opt5: 0.110533
+    Opt5: 0.113798
 
 
 
@@ -753,7 +753,7 @@ Futhermore, we can also utilize multi-core processors to do the thread-level par
 
  .. code-block:: none
 
-    Opt6: 0.145002
+    Opt6: 0.147472
 
 
 
diff --git a/docs/_sources/how_to/optimize_operators/sg_execution_times.rst.txt b/docs/_sources/how_to/optimize_operators/sg_execution_times.rst.txt
index 8c2f0c6de..b6204f5c9 100644
--- a/docs/_sources/how_to/optimize_operators/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/optimize_operators/sg_execution_times.rst.txt
@@ -5,8 +5,8 @@
 
 Computation times
 =================
-**00:34.973** total execution time for **how_to_optimize_operators** files:
+**00:35.818** total execution time for **how_to_optimize_operators** files:
 
-- **00:32.353**: :ref:`sphx_glr_how_to_optimize_operators_opt_gemm.py` (``opt_gemm.py``)
-- **00:01.398**: :ref:`sphx_glr_how_to_optimize_operators_opt_conv_tensorcore.py` (``opt_conv_tensorcore.py``)
-- **00:01.221**: :ref:`sphx_glr_how_to_optimize_operators_opt_conv_cuda.py` (``opt_conv_cuda.py``)
+- **00:33.099**: :ref:`sphx_glr_how_to_optimize_operators_opt_gemm.py` (``opt_gemm.py``)
+- **00:01.461**: :ref:`sphx_glr_how_to_optimize_operators_opt_conv_tensorcore.py` (``opt_conv_tensorcore.py``)
+- **00:01.258**: :ref:`sphx_glr_how_to_optimize_operators_opt_conv_cuda.py` (``opt_conv_cuda.py``)
diff --git a/docs/_sources/how_to/tune_with_autoscheduler/sg_execution_times.rst.txt b/docs/_sources/how_to/tune_with_autoscheduler/sg_execution_times.rst.txt
index 311b5c1e8..d239a8924 100644
--- a/docs/_sources/how_to/tune_with_autoscheduler/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/tune_with_autoscheduler/sg_execution_times.rst.txt
@@ -5,11 +5,11 @@
 
 Computation times
 =================
-**04:53.427** total execution time for **how_to_tune_with_autoscheduler** files:
-
-- **02:20.154**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_conv2d_layer_cuda.py` (``tune_conv2d_layer_cuda.py``)
-- **01:18.957**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_x86.py` (``tune_network_x86.py``)
-- **00:40.191**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_cuda.py` (``tune_network_cuda.py``)
-- **00:17.223**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_sparse_x86.py` (``tune_sparse_x86.py``)
-- **00:08.638**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_mali.py` (``tune_network_mali.py``)
-- **00:08.264**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_arm.py` (``tune_network_arm.py``)
+**04:58.445** total execution time for **how_to_tune_with_autoscheduler** files:
+
+- **02:21.500**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_conv2d_layer_cuda.py` (``tune_conv2d_layer_cuda.py``)
+- **01:21.317**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_x86.py` (``tune_network_x86.py``)
+- **00:41.395**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_cuda.py` (``tune_network_cuda.py``)
+- **00:16.070**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_sparse_x86.py` (``tune_sparse_x86.py``)
+- **00:09.281**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_mali.py` (``tune_network_mali.py``)
+- **00:08.880**: :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_arm.py` (``tune_network_arm.py``)
diff --git a/docs/_sources/how_to/tune_with_autoscheduler/tune_conv2d_layer_cuda.rst.txt b/docs/_sources/how_to/tune_with_autoscheduler/tune_conv2d_layer_cuda.rst.txt
index 0254c8e03..f7ff3c808 100644
--- a/docs/_sources/how_to/tune_with_autoscheduler/tune_conv2d_layer_cuda.rst.txt
+++ b/docs/_sources/how_to/tune_with_autoscheduler/tune_conv2d_layer_cuda.rst.txt
@@ -221,350 +221,498 @@ cooperative fetching, unrolling and operator fusion.
                  bias: Buffer(bias_2: Pointer(float32), float32, [512], []),
                  compute: Buffer(compute_2: Pointer(float32), float32, [25088], [])}
       buffer_map = {data_1: data, kernel_1: kernel, bias_1: bias, compute_1: compute} {
-      attr [IterVar(blockIdx.x: int32, (nullptr), "ThreadIndex", "blockIdx.x")] "thread_extent" = 32;
-      allocate(conv2d_nchw: Pointer(local float32), float32, [28]), storage_scope = local;
-      allocate(pad_temp.shared: Pointer(shared float32), float32, [504]), storage_scope = shared;
+      attr [IterVar(blockIdx.x: int32, (nullptr), "ThreadIndex", "blockIdx.x")] "thread_extent" = 224;
+      allocate(conv2d_nchw: Pointer(local float32), float32, [14]), storage_scope = local;
+      allocate(pad_temp.shared: Pointer(shared float32), float32, [72]), storage_scope = shared;
       allocate(kernel.shared: Pointer(shared float32), float32, [384]), storage_scope = shared;
-      attr [IterVar(threadIdx.x: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 28 {
-        conv2d_nchw_1: Buffer(conv2d_nchw, float32, [16], [], scope="local", align=16)[0] = 0f32
-        conv2d_nchw_1[4] = 0f32
-        conv2d_nchw_1[8] = 0f32
-        conv2d_nchw_1[12] = 0f32
-        conv2d_nchw_1[16] = 0f32
-        conv2d_nchw_1[20] = 0f32
-        conv2d_nchw_1[24] = 0f32
+      attr [IterVar(threadIdx.x: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 8 {
+        conv2d_nchw_1: Buffer(conv2d_nchw, float32, [1], [], scope="local", align=4)[0] = 0f32
         conv2d_nchw_1[1] = 0f32
-        conv2d_nchw_1[5] = 0f32
-        conv2d_nchw_1[9] = 0f32
-        conv2d_nchw_1[13] = 0f32
-        conv2d_nchw_1[17] = 0f32
-        conv2d_nchw_1[21] = 0f32
-        conv2d_nchw_1[25] = 0f32
         conv2d_nchw_1[2] = 0f32
-        conv2d_nchw_1[6] = 0f32
-        conv2d_nchw_1[10] = 0f32
-        conv2d_nchw_1[14] = 0f32
-        conv2d_nchw_1[18] = 0f32
-        conv2d_nchw_1[22] = 0f32
-        conv2d_nchw_1[26] = 0f32
         conv2d_nchw_1[3] = 0f32
+        conv2d_nchw_1[4] = 0f32
+        conv2d_nchw_1[5] = 0f32
+        conv2d_nchw_1[6] = 0f32
         conv2d_nchw_1[7] = 0f32
+        conv2d_nchw_1[8] = 0f32
+        conv2d_nchw_1[9] = 0f32
+        conv2d_nchw_1[10] = 0f32
         conv2d_nchw_1[11] = 0f32
-        conv2d_nchw_1[15] = 0f32
-        conv2d_nchw_1[19] = 0f32
-        conv2d_nchw_1[23] = 0f32
-        conv2d_nchw_1[27] = 0f32
+        conv2d_nchw_1[12] = 0f32
+        conv2d_nchw_1[13] = 0f32
         for (rc.outer.outer: int32, 0, 64) {
           for (ry.outer.outer: int32, 0, 3) {
-            let cse_var_4: int32 = (rc.outer.outer*392)
-            let cse_var_3: int32 = (ry.outer.outer*7)
-            let cse_var_2: int32 = (rc.outer.outer*72)
+            let cse_var_3: int32 = (rc.outer.outer*392)
+            let cse_var_2: int32 = (ry.outer.outer*7)
             let cse_var_1: int32 = (ry.outer.outer*3)
              {
-              attr [IterVar(threadIdx.x_1: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 28;
-              pad_temp.shared_1: Buffer(pad_temp.shared, float32, [504], [], scope="shared")[threadIdx.x_1] = @tir.if_then_else((((1 <= (floordiv(threadIdx.x_1, 9) + ry.outer.outer)) && (1 <= floormod(threadIdx.x_1, 9))) && (floormod(threadIdx.x_1, 9) < 8)), data[((((cse_var_4 + (floordiv(threadIdx.x_1, 9)*7)) + cse_var_3) + floormod(threadIdx.x_1, 9)) - 8)], 0f32, dtype=float32)
-              attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 28;
-              pad_temp.shared_1[(threadIdx.x_1 + 28)] = @tir.if_then_else(((((floordiv((threadIdx.x_1 + 28), 9) + ry.outer.outer) < 8) && (1 <= floormod((threadIdx.x_1 + 1), 9))) && (floormod((threadIdx.x_1 + 1), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 28), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 1), 9)) - 8)], 0f32, dtype=float32)
-              attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 28;
-              pad_temp.shared_1[(threadIdx.x_1 + 56)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 56), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 56), 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod((threadIdx.x_1 + 2), 9))) && (floormod((threadIdx.x_1 + 2), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 56), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 2), 9)) - 8)], 0f32, dtype=float32)
-              attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 28;
-              pad_temp.shared_1[(threadIdx.x_1 + 84)] = @tir.if_then_else(((1 <= floormod((threadIdx.x_1 + 3), 9)) && (floormod((threadIdx.x_1 + 3), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 84), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 3), 9)) - 8)], 0f32, dtype=float32)
-              attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 28;
-              pad_temp.shared_1[(threadIdx.x_1 + 112)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 112), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 112), 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod((threadIdx.x_1 + 4), 9))) && (floormod((threadIdx.x_1 + 4), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 112), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 4), 9)) - 8)], 0f32, dtype=float32)
-              attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 28;
-              pad_temp.shared_1[(threadIdx.x_1 + 140)] = @tir.if_then_else(((1 <= floormod((threadIdx.x_1 + 5), 9)) && (floormod((threadIdx.x_1 + 5), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 140), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 5), 9)) - 8)], 0f32, dtype=float32)
-              attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 28;
-              pad_temp.shared_1[(threadIdx.x_1 + 168)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 168), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 168), 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod((threadIdx.x_1 + 6), 9))) && (floormod((threadIdx.x_1 + 6), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 168), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 6), 9)) - 8)], 0f32, dtype=float32)
-              attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 28;
-              pad_temp.shared_1[(threadIdx.x_1 + 196)] = @tir.if_then_else((((1 <= (floordiv(floormod((threadIdx.x_1 + 196), 63), 9) + ry.outer.outer)) && (1 <= floormod((threadIdx.x_1 + 7), 9))) && (floormod((threadIdx.x_1 + 7), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 196), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 7), 9)) - 8)], 0f32, dtype=float32)
-              attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 28;
-              pad_temp.shared_1[(threadIdx.x_1 + 224)] = @tir.if_then_else(((((floordiv(floormod((threadIdx.x_1 + 224), 63), 9) + ry.outer.outer) < 8) && (1 <= floormod((threadIdx.x_1 + 8), 9))) && (floormod((threadIdx.x_1 + 8), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 224), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 8), 9)) - 8)], 0f32, dtype=float32)
-              attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 28;
-              pad_temp.shared_1[(threadIdx.x_1 + 252)] = @tir.if_then_else((((1 <= (floordiv(threadIdx.x_1, 9) + ry.outer.outer)) && (1 <= floormod(threadIdx.x_1, 9))) && (floormod(threadIdx.x_1, 9) < 8)), data[((((cse_var_4 + (floordiv(threadIdx.x_1, 9)*7)) + cse_var_3) + floormod(threadIdx.x_1, 9)) + 188)], 0f32, dtype=float32)
-              attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 28;
-              pad_temp.shared_1[(threadIdx.x_1 + 280)] = @tir.if_then_else(((((floordiv(floormod((threadIdx.x_1 + 280), 63), 9) + ry.outer.outer) < 8) && (1 <= floormod((threadIdx.x_1 + 1), 9))) && (floormod((threadIdx.x_1 + 1), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 280), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 1), 9)) - 8)], 0f32, dtype=float32)
-              attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 28;
-              pad_temp.shared_1[(threadIdx.x_1 + 308)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 308), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 308), 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod((threadIdx.x_1 + 2), 9))) && (floormod((threadIdx.x_1 + 2), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 308), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 2), 9)) - 8)], 0f32, dtype=float32)
-              attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 28;
-              pad_temp.shared_1[(threadIdx.x_1 + 336)] = @tir.if_then_else(((1 <= floormod((threadIdx.x_1 + 3), 9)) && (floormod((threadIdx.x_1 + 3), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 336), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 3), 9)) - 8)], 0f32, dtype=float32)
-              attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 28;
-              pad_temp.shared_1[(threadIdx.x_1 + 364)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 364), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 364), 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod((threadIdx.x_1 + 4), 9))) && (floormod((threadIdx.x_1 + 4), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 364), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 4), 9)) - 8)], 0f32, dtype=float32)
-              attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 28;
-              pad_temp.shared_1[(threadIdx.x_1 + 392)] = @tir.if_then_else(((1 <= floormod((threadIdx.x_1 + 5), 9)) && (floormod((threadIdx.x_1 + 5), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 392), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 5), 9)) - 8)], 0f32, dtype=float32)
-              attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 28;
-              pad_temp.shared_1[(threadIdx.x_1 + 420)] = @tir.if_then_else(((((1 <= (floordiv(floormod((threadIdx.x_1 + 420), 63), 9) + ry.outer.outer)) && ((floordiv(floormod((threadIdx.x_1 + 420), 63), 9) + ry.outer.outer) < 8)) && (1 <= floormod((threadIdx.x_1 + 6), 9))) && (floormod((threadIdx.x_1 + 6), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 420), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 6), 9)) - 8)], 0f32, dtype=float32)
-              attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 28;
-              pad_temp.shared_1[(threadIdx.x_1 + 448)] = @tir.if_then_else((((1 <= (floordiv(floormod((threadIdx.x_1 + 448), 63), 9) + ry.outer.outer)) && (1 <= floormod((threadIdx.x_1 + 7), 9))) && (floormod((threadIdx.x_1 + 7), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 448), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 7), 9)) - 8)], 0f32, dtype=float32)
-              attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 28;
-              pad_temp.shared_1[(threadIdx.x_1 + 476)] = @tir.if_then_else(((((floordiv(floormod((threadIdx.x_1 + 476), 63), 9) + ry.outer.outer) < 8) && (1 <= floormod((threadIdx.x_1 + 8), 9))) && (floormod((threadIdx.x_1 + 8), 9) < 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 476), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 8), 9)) - 8)], 0f32, dtype=float32)
-              attr [IterVar(threadIdx.x_2: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 28;
-              kernel.shared_1: Buffer(kernel.shared, float32, [384], [], scope="shared")[threadIdx.x_2] = kernel[((((((blockIdx.x*73728) + (floordiv(threadIdx.x_2, 24)*4608)) + cse_var_2) + (floordiv(floormod(threadIdx.x_2, 24), 3)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3))]
-              attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 28;
-              kernel.shared_1[(threadIdx.x_2 + 28)] = kernel[((((((blockIdx.x*73728) + (floordiv((floordiv(threadIdx.x_2, 4) + 7), 6)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 28), 24), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3))]
-              attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 28;
-              kernel.shared_1[(threadIdx.x_2 + 56)] = kernel[((((((blockIdx.x*73728) + (floordiv((floordiv(threadIdx.x_2, 4) + 14), 6)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 56), 24), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3))]
-              attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 28;
-              kernel.shared_1[(threadIdx.x_2 + 84)] = kernel[((((((blockIdx.x*73728) + (floordiv((floordiv(threadIdx.x_2, 4) + 21), 6)*4608)) + cse_var_2) + (floormod((floordiv(threadIdx.x_2, 3) + 4), 8)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3))]
-              attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 28;
-              kernel.shared_1[(threadIdx.x_2 + 112)] = kernel[((((((blockIdx.x*73728) + (floordiv((floordiv(threadIdx.x_2, 4) + 28), 6)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 112), 24), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3))]
-              attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 28;
-              kernel.shared_1[(threadIdx.x_2 + 140)] = kernel[((((((blockIdx.x*73728) + (floordiv((floordiv(threadIdx.x_2, 4) + 35), 6)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 140), 24), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3))]
-              attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 28;
-              kernel.shared_1[(threadIdx.x_2 + 168)] = kernel[(((((((blockIdx.x*73728) + (floordiv(floordiv(threadIdx.x_2, 4), 6)*4608)) + cse_var_2) + (floordiv(floormod(threadIdx.x_2, 24), 3)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3)) + 32256)]
-              attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 28;
-              kernel.shared_1[(threadIdx.x_2 + 196)] = kernel[((((((blockIdx.x*73728) + (floordiv((floordiv(threadIdx.x_2, 4) + 49), 6)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 196), 24), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3))]
-              attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 28;
-              kernel.shared_1[(threadIdx.x_2 + 224)] = kernel[((((((blockIdx.x*73728) + (floordiv((floordiv(threadIdx.x_2, 4) + 56), 6)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 224), 24), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3))]
-              attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 28;
-              kernel.shared_1[(threadIdx.x_2 + 252)] = kernel[((((((blockIdx.x*73728) + (floordiv((floordiv(threadIdx.x_2, 4) + 63), 6)*4608)) + cse_var_2) + (floormod((floordiv(threadIdx.x_2, 3) + 4), 8)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3))]
-              attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 28;
-              kernel.shared_1[(threadIdx.x_2 + 280)] = kernel[((((((blockIdx.x*73728) + (floordiv((floordiv(threadIdx.x_2, 4) + 70), 6)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 280), 24), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3))]
-              attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 28;
-              kernel.shared_1[(threadIdx.x_2 + 308)] = kernel[((((((blockIdx.x*73728) + (floordiv((floordiv(threadIdx.x_2, 4) + 77), 6)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 308), 24), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3))]
-              attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 28;
-              kernel.shared_1[(threadIdx.x_2 + 336)] = kernel[(((((((blockIdx.x*73728) + (floordiv(floordiv(threadIdx.x_2, 4), 6)*4608)) + cse_var_2) + (floordiv(floormod(threadIdx.x_2, 24), 3)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3)) + 64512)]
-              attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 28;
-              if @tir.likely((threadIdx.x_2 < 20), dtype=bool) {
-                kernel.shared_1[(threadIdx.x_2 + 364)] = kernel[((((((blockIdx.x*73728) + (floordiv((floordiv(threadIdx.x_2, 4) + 91), 6)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 364), 24), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3))]
-              }
-              for (rx.outer.inner: int32, 0, 3) {
-                conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(rx.outer.inner + floormod(threadIdx.x, 7))]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + rx.outer.inner)]))
-                conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 9)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + rx.outer.inner)]))
-                conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 18)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + rx.outer.inner)]))
-                conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 27)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + rx.outer.inner)]))
-                conv2d_nchw_1[16] = (conv2d_nchw_1[16] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 36)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + rx.outer.inner)]))
-                conv2d_nchw_1[20] = (conv2d_nchw_1[20] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 45)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + rx.outer.inner)]))
-                conv2d_nchw_1[24] = (conv2d_nchw_1[24] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 54)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + rx.outer.inner)]))
-                conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 63)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 3)]))
-                conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 72)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 3)]))
-                conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 81)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 3)]))
-                conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 90)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 3)]))
-                conv2d_nchw_1[16] = (conv2d_nchw_1[16] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 99)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 3)]))
-                conv2d_nchw_1[20] = (conv2d_nchw_1[20] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 108)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 3)]))
-                conv2d_nchw_1[24] = (conv2d_nchw_1[24] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 117)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 3)]))
-                conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 126)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 6)]))
-                conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 135)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 6)]))
-                conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 144)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 6)]))
-                conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 153)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 6)]))
-                conv2d_nchw_1[16] = (conv2d_nchw_1[16] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 162)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 6)]))
-                conv2d_nchw_1[20] = (conv2d_nchw_1[20] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 171)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 6)]))
-                conv2d_nchw_1[24] = (conv2d_nchw_1[24] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 180)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 6)]))
-                conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 189)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 9)]))
-                conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 198)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 9)]))
-                conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 207)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 9)]))
-                conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 216)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 9)]))
-                conv2d_nchw_1[16] = (conv2d_nchw_1[16] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 225)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 9)]))
-                conv2d_nchw_1[20] = (conv2d_nchw_1[20] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 234)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 9)]))
-                conv2d_nchw_1[24] = (conv2d_nchw_1[24] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 243)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 9)]))
-                conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 252)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 12)]))
-                conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 261)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 12)]))
-                conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 270)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 12)]))
-                conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 279)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 12)]))
-                conv2d_nchw_1[16] = (conv2d_nchw_1[16] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 288)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 12)]))
-                conv2d_nchw_1[20] = (conv2d_nchw_1[20] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 297)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 12)]))
-                conv2d_nchw_1[24] = (conv2d_nchw_1[24] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 306)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 12)]))
-                conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 315)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 15)]))
-                conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 324)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 15)]))
-                conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 333)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 15)]))
-                conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 342)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 15)]))
-                conv2d_nchw_1[16] = (conv2d_nchw_1[16] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 351)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 15)]))
-                conv2d_nchw_1[20] = (conv2d_nchw_1[20] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 360)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 15)]))
-                conv2d_nchw_1[24] = (conv2d_nchw_1[24] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 369)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 15)]))
-                conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 378)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 18)]))
-                conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 387)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 18)]))
-                conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 396)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 18)]))
-                conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 405)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 18)]))
-                conv2d_nchw_1[16] = (conv2d_nchw_1[16] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 414)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 18)]))
-                conv2d_nchw_1[20] = (conv2d_nchw_1[20] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 423)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 18)]))
-                conv2d_nchw_1[24] = (conv2d_nchw_1[24] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 432)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 18)]))
-                conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 441)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 21)]))
-                conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 450)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 21)]))
-                conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 459)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 21)]))
-                conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 468)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 21)]))
-                conv2d_nchw_1[16] = (conv2d_nchw_1[16] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 477)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 21)]))
-                conv2d_nchw_1[20] = (conv2d_nchw_1[20] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 486)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 21)]))
-                conv2d_nchw_1[24] = (conv2d_nchw_1[24] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 495)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 21)]))
-                conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(rx.outer.inner + floormod(threadIdx.x, 7))]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 24)]))
-                conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 9)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 24)]))
-                conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 18)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 24)]))
-                conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 27)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 24)]))
-                conv2d_nchw_1[17] = (conv2d_nchw_1[17] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 36)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 24)]))
-                conv2d_nchw_1[21] = (conv2d_nchw_1[21] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 45)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 24)]))
-                conv2d_nchw_1[25] = (conv2d_nchw_1[25] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 54)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 24)]))
-                conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 63)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 27)]))
-                conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 72)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 27)]))
-                conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 81)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 27)]))
-                conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 90)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 27)]))
-                conv2d_nchw_1[17] = (conv2d_nchw_1[17] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 99)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 27)]))
-                conv2d_nchw_1[21] = (conv2d_nchw_1[21] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 108)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 27)]))
-                conv2d_nchw_1[25] = (conv2d_nchw_1[25] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 117)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 27)]))
-                conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 126)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 30)]))
-                conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 135)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 30)]))
-                conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 144)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 30)]))
-                conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 153)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 30)]))
-                conv2d_nchw_1[17] = (conv2d_nchw_1[17] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 162)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 30)]))
-                conv2d_nchw_1[21] = (conv2d_nchw_1[21] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 171)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 30)]))
-                conv2d_nchw_1[25] = (conv2d_nchw_1[25] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 180)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 30)]))
-                conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 189)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 33)]))
-                conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 198)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 33)]))
-                conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 207)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 33)]))
-                conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 216)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 33)]))
-                conv2d_nchw_1[17] = (conv2d_nchw_1[17] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 225)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 33)]))
-                conv2d_nchw_1[21] = (conv2d_nchw_1[21] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 234)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 33)]))
-                conv2d_nchw_1[25] = (conv2d_nchw_1[25] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 243)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 33)]))
-                conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 252)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 36)]))
-                conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 261)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 36)]))
-                conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 270)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 36)]))
-                conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 279)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 36)]))
-                conv2d_nchw_1[17] = (conv2d_nchw_1[17] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 288)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 36)]))
-                conv2d_nchw_1[21] = (conv2d_nchw_1[21] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 297)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 36)]))
-                conv2d_nchw_1[25] = (conv2d_nchw_1[25] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 306)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 36)]))
-                conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 315)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 39)]))
-                conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 324)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 39)]))
-                conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 333)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 39)]))
-                conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 342)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 39)]))
-                conv2d_nchw_1[17] = (conv2d_nchw_1[17] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 351)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 39)]))
-                conv2d_nchw_1[21] = (conv2d_nchw_1[21] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 360)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 39)]))
-                conv2d_nchw_1[25] = (conv2d_nchw_1[25] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 369)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 39)]))
-                conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 378)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 42)]))
-                conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 387)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 42)]))
-                conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 396)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 42)]))
-                conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 405)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 42)]))
-                conv2d_nchw_1[17] = (conv2d_nchw_1[17] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 414)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 42)]))
-                conv2d_nchw_1[21] = (conv2d_nchw_1[21] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 423)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 42)]))
-                conv2d_nchw_1[25] = (conv2d_nchw_1[25] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 432)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 42)]))
-                conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 441)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 45)]))
-                conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 450)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 45)]))
-                conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 459)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 45)]))
-                conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 468)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 45)]))
-                conv2d_nchw_1[17] = (conv2d_nchw_1[17] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 477)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 45)]))
-                conv2d_nchw_1[21] = (conv2d_nchw_1[21] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 486)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 45)]))
-                conv2d_nchw_1[25] = (conv2d_nchw_1[25] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 495)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 45)]))
-                conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(rx.outer.inner + floormod(threadIdx.x, 7))]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 48)]))
-                conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 9)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 48)]))
-                conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 18)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 48)]))
-                conv2d_nchw_1[14] = (conv2d_nchw_1[14] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 27)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 48)]))
-                conv2d_nchw_1[18] = (conv2d_nchw_1[18] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 36)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 48)]))
-                conv2d_nchw_1[22] = (conv2d_nchw_1[22] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 45)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 48)]))
-                conv2d_nchw_1[26] = (conv2d_nchw_1[26] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 54)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 48)]))
-                conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 63)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 51)]))
-                conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 72)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 51)]))
-                conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 81)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 51)]))
-                conv2d_nchw_1[14] = (conv2d_nchw_1[14] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 90)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 51)]))
-                conv2d_nchw_1[18] = (conv2d_nchw_1[18] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 99)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 51)]))
-                conv2d_nchw_1[22] = (conv2d_nchw_1[22] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 108)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 51)]))
-                conv2d_nchw_1[26] = (conv2d_nchw_1[26] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 117)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 51)]))
-                conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 126)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 54)]))
-                conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 135)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 54)]))
-                conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 144)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 54)]))
-                conv2d_nchw_1[14] = (conv2d_nchw_1[14] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 153)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 54)]))
-                conv2d_nchw_1[18] = (conv2d_nchw_1[18] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 162)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 54)]))
-                conv2d_nchw_1[22] = (conv2d_nchw_1[22] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 171)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 54)]))
-                conv2d_nchw_1[26] = (conv2d_nchw_1[26] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 180)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 54)]))
-                conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 189)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 57)]))
-                conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 198)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 57)]))
-                conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 207)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 57)]))
-                conv2d_nchw_1[14] = (conv2d_nchw_1[14] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 216)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 57)]))
-                conv2d_nchw_1[18] = (conv2d_nchw_1[18] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 225)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 57)]))
-                conv2d_nchw_1[22] = (conv2d_nchw_1[22] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 234)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 57)]))
-                conv2d_nchw_1[26] = (conv2d_nchw_1[26] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 243)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 57)]))
-                conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 252)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 60)]))
-                conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 261)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 60)]))
-                conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 270)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 60)]))
-                conv2d_nchw_1[14] = (conv2d_nchw_1[14] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 279)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 60)]))
-                conv2d_nchw_1[18] = (conv2d_nchw_1[18] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 288)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 60)]))
-                conv2d_nchw_1[22] = (conv2d_nchw_1[22] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 297)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 60)]))
-                conv2d_nchw_1[26] = (conv2d_nchw_1[26] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 306)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 60)]))
-                conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 315)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 63)]))
-                conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 324)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 63)]))
-                conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 333)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 63)]))
-                conv2d_nchw_1[14] = (conv2d_nchw_1[14] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 342)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 63)]))
-                conv2d_nchw_1[18] = (conv2d_nchw_1[18] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 351)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 63)]))
-                conv2d_nchw_1[22] = (conv2d_nchw_1[22] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 360)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 63)]))
-                conv2d_nchw_1[26] = (conv2d_nchw_1[26] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 369)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 63)]))
-                conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 378)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 66)]))
-                conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 387)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 66)]))
-                conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 396)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 66)]))
-                conv2d_nchw_1[14] = (conv2d_nchw_1[14] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 405)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 66)]))
-                conv2d_nchw_1[18] = (conv2d_nchw_1[18] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 414)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 66)]))
-                conv2d_nchw_1[22] = (conv2d_nchw_1[22] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 423)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 66)]))
-                conv2d_nchw_1[26] = (conv2d_nchw_1[26] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 432)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 66)]))
-                conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 441)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 69)]))
-                conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 450)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 69)]))
-                conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 459)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 69)]))
-                conv2d_nchw_1[14] = (conv2d_nchw_1[14] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 468)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 69)]))
-                conv2d_nchw_1[18] = (conv2d_nchw_1[18] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 477)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 69)]))
-                conv2d_nchw_1[22] = (conv2d_nchw_1[22] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 486)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 69)]))
-                conv2d_nchw_1[26] = (conv2d_nchw_1[26] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 495)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 69)]))
-                conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(rx.outer.inner + floormod(threadIdx.x, 7))]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 72)]))
-                conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 9)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 72)]))
-                conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 18)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 72)]))
-                conv2d_nchw_1[15] = (conv2d_nchw_1[15] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 27)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 72)]))
-                conv2d_nchw_1[19] = (conv2d_nchw_1[19] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 36)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 72)]))
-                conv2d_nchw_1[23] = (conv2d_nchw_1[23] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 45)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 72)]))
-                conv2d_nchw_1[27] = (conv2d_nchw_1[27] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 54)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 72)]))
-                conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 63)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 75)]))
-                conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 72)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 75)]))
-                conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 81)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 75)]))
-                conv2d_nchw_1[15] = (conv2d_nchw_1[15] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 90)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 75)]))
-                conv2d_nchw_1[19] = (conv2d_nchw_1[19] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 99)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 75)]))
-                conv2d_nchw_1[23] = (conv2d_nchw_1[23] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 108)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 75)]))
-                conv2d_nchw_1[27] = (conv2d_nchw_1[27] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 117)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 75)]))
-                conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 126)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 78)]))
-                conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 135)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 78)]))
-                conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 144)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 78)]))
-                conv2d_nchw_1[15] = (conv2d_nchw_1[15] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 153)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 78)]))
-                conv2d_nchw_1[19] = (conv2d_nchw_1[19] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 162)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 78)]))
-                conv2d_nchw_1[23] = (conv2d_nchw_1[23] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 171)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 78)]))
-                conv2d_nchw_1[27] = (conv2d_nchw_1[27] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 180)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 78)]))
-                conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 189)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 81)]))
-                conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 198)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 81)]))
-                conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 207)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 81)]))
-                conv2d_nchw_1[15] = (conv2d_nchw_1[15] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 216)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 81)]))
-                conv2d_nchw_1[19] = (conv2d_nchw_1[19] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 225)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 81)]))
-                conv2d_nchw_1[23] = (conv2d_nchw_1[23] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 234)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 81)]))
-                conv2d_nchw_1[27] = (conv2d_nchw_1[27] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 243)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 81)]))
-                conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 252)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 84)]))
-                conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 261)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 84)]))
-                conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 270)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 84)]))
-                conv2d_nchw_1[15] = (conv2d_nchw_1[15] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 279)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 84)]))
-                conv2d_nchw_1[19] = (conv2d_nchw_1[19] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 288)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 84)]))
-                conv2d_nchw_1[23] = (conv2d_nchw_1[23] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 297)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 84)]))
-                conv2d_nchw_1[27] = (conv2d_nchw_1[27] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 306)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 84)]))
-                conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 315)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 87)]))
-                conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 324)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 87)]))
-                conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 333)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 87)]))
-                conv2d_nchw_1[15] = (conv2d_nchw_1[15] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 342)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 87)]))
-                conv2d_nchw_1[19] = (conv2d_nchw_1[19] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 351)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 87)]))
-                conv2d_nchw_1[23] = (conv2d_nchw_1[23] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 360)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 87)]))
-                conv2d_nchw_1[27] = (conv2d_nchw_1[27] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 369)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 87)]))
-                conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 378)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 90)]))
-                conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 387)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 90)]))
-                conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 396)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 90)]))
-                conv2d_nchw_1[15] = (conv2d_nchw_1[15] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 405)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 90)]))
-                conv2d_nchw_1[19] = (conv2d_nchw_1[19] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 414)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 90)]))
-                conv2d_nchw_1[23] = (conv2d_nchw_1[23] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 423)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 90)]))
-                conv2d_nchw_1[27] = (conv2d_nchw_1[27] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 432)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 90)]))
-                conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 441)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 93)]))
-                conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 450)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 93)]))
-                conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 459)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 93)]))
-                conv2d_nchw_1[15] = (conv2d_nchw_1[15] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 468)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 93)]))
-                conv2d_nchw_1[19] = (conv2d_nchw_1[19] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 477)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 93)]))
-                conv2d_nchw_1[23] = (conv2d_nchw_1[23] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 486)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 93)]))
-                conv2d_nchw_1[27] = (conv2d_nchw_1[27] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 495)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 93)]))
-              }
+              attr [IterVar(threadIdx.x_1: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 8;
+              pad_temp.shared_1: Buffer(pad_temp.shared, float32, [72], [], scope="shared")[threadIdx.x_1] = @tir.if_then_else((((1 <= (ry.outer.outer + floormod(blockIdx.x, 7))) && ((ry.outer.outer + floormod(blockIdx.x, 7)) < 8)) && (1 <= threadIdx.x_1)), data[((((cse_var_3 + cse_var_2) + (floormod(blockIdx.x, 7)*7)) + threadIdx.x_1) - 8)], 0f32, dtype=float32)
+              attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 8;
+              pad_temp.shared_1[(threadIdx.x_1 + 8)] = @tir.if_then_else(((((1 <= (ry.outer.outer + floormod(blockIdx.x, 7))) && ((ry.outer.outer + floormod(blockIdx.x, 7)) < 8)) && (1 <= floormod((threadIdx.x_1 + 8), 9))) && (floormod((threadIdx.x_1 + 8), 9) < 8)), data[(((((cse_var_3 + (floordiv((threadIdx.x_1 + 8), 9)*49)) + cse_var_2) + (floormod(blockIdx.x, 7)*7)) + floormod((threadIdx.x_1 + 8), 9)) - 8)], 0f32, dtype=float32)
+              attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 8;
+              pad_temp.shared_1[(threadIdx.x_1 + 16)] = @tir.if_then_else(((((1 <= (ry.outer.outer + floormod(blockIdx.x, 7))) && ((ry.outer.outer + floormod(blockIdx.x, 7)) < 8)) && (1 <= floormod((threadIdx.x_1 + 7), 9))) && (floormod((threadIdx.x_1 + 7), 9) < 8)), data[(((((cse_var_3 + (floordiv((threadIdx.x_1 + 16), 9)*49)) + cse_var_2) + (floormod(blockIdx.x, 7)*7)) + floormod((threadIdx.x_1 + 7), 9)) - 8)], 0f32, dtype=float32)
+              attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 8;
+              pad_temp.shared_1[(threadIdx.x_1 + 24)] = @tir.if_then_else(((((1 <= (ry.outer.outer + floormod(blockIdx.x, 7))) && ((ry.outer.outer + floormod(blockIdx.x, 7)) < 8)) && (1 <= floormod((threadIdx.x_1 + 6), 9))) && (floormod((threadIdx.x_1 + 6), 9) < 8)), data[(((((cse_var_3 + (floordiv((threadIdx.x_1 + 24), 9)*49)) + cse_var_2) + (floormod(blockIdx.x, 7)*7)) + floormod((threadIdx.x_1 + 6), 9)) - 8)], 0f32, dtype=float32)
+              attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 8;
+              pad_temp.shared_1[(threadIdx.x_1 + 32)] = @tir.if_then_else(((((1 <= (ry.outer.outer + floormod(blockIdx.x, 7))) && ((ry.outer.outer + floormod(blockIdx.x, 7)) < 8)) && (1 <= floormod((threadIdx.x_1 + 5), 9))) && (floormod((threadIdx.x_1 + 5), 9) < 8)), data[(((((cse_var_3 + (floordiv((threadIdx.x_1 + 32), 9)*49)) + cse_var_2) + (floormod(blockIdx.x, 7)*7)) + floormod((threadIdx.x_1 + 5), 9)) - 8)], 0f32, dtype=float32)
+              attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 8;
+              pad_temp.shared_1[(threadIdx.x_1 + 40)] = @tir.if_then_else(((((1 <= (ry.outer.outer + floormod(blockIdx.x, 7))) && ((ry.outer.outer + floormod(blockIdx.x, 7)) < 8)) && (1 <= floormod((threadIdx.x_1 + 4), 9))) && (floormod((threadIdx.x_1 + 4), 9) < 8)), data[(((((cse_var_3 + (floordiv((threadIdx.x_1 + 40), 9)*49)) + cse_var_2) + (floormod(blockIdx.x, 7)*7)) + floormod((threadIdx.x_1 + 4), 9)) - 8)], 0f32, dtype=float32)
+              attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 8;
+              pad_temp.shared_1[(threadIdx.x_1 + 48)] = @tir.if_then_else(((((1 <= (ry.outer.outer + floormod(blockIdx.x, 7))) && ((ry.outer.outer + floormod(blockIdx.x, 7)) < 8)) && (1 <= floormod((threadIdx.x_1 + 3), 9))) && (floormod((threadIdx.x_1 + 3), 9) < 8)), data[(((((cse_var_3 + (floordiv((threadIdx.x_1 + 48), 9)*49)) + cse_var_2) + (floormod(blockIdx.x, 7)*7)) + floormod((threadIdx.x_1 + 3), 9)) - 8)], 0f32, dtype=float32)
+              attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 8;
+              pad_temp.shared_1[(threadIdx.x_1 + 56)] = @tir.if_then_else(((((1 <= (ry.outer.outer + floormod(blockIdx.x, 7))) && ((ry.outer.outer + floormod(blockIdx.x, 7)) < 8)) && (1 <= floormod((threadIdx.x_1 + 2), 9))) && (floormod((threadIdx.x_1 + 2), 9) < 8)), data[(((((cse_var_3 + (floordiv((threadIdx.x_1 + 56), 9)*49)) + cse_var_2) + (floormod(blockIdx.x, 7)*7)) + floormod((threadIdx.x_1 + 2), 9)) - 8)], 0f32, dtype=float32)
+              attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 8;
+              pad_temp.shared_1[(threadIdx.x_1 + 64)] = @tir.if_then_else((((1 <= (ry.outer.outer + floormod(blockIdx.x, 7))) && ((ry.outer.outer + floormod(blockIdx.x, 7)) < 8)) && (floormod((threadIdx.x_1 + 1), 9) < 8)), data[(((((cse_var_3 + (floordiv((threadIdx.x_1 + 64), 9)*49)) + cse_var_2) + (floormod(blockIdx.x, 7)*7)) + floormod((threadIdx.x_1 + 1), 9)) - 8)], 0f32, dtype=float32)
+              attr [IterVar(threadIdx.x_2: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 8;
+              kernel.shared_1: Buffer(kernel.shared, float32, [384], [], scope="shared")[threadIdx.x_2] = kernel[(((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv(threadIdx.x_2, 3)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3))]
+              attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 8;
+              kernel.shared_1[(threadIdx.x_2 + 8)] = kernel[(((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv((threadIdx.x_2 + 8), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3))]
+              attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 8;
+              kernel.shared_1[(threadIdx.x_2 + 16)] = kernel[(((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv((threadIdx.x_2 + 16), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3))]
+              attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 8;
+              kernel.shared_1[(threadIdx.x_2 + 24)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv(threadIdx.x_2, 3)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3)) + 4608)]
+              attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 8;
+              kernel.shared_1[(threadIdx.x_2 + 32)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv((threadIdx.x_2 + 8), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3)) + 4608)]
+              attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 8;
+              kernel.shared_1[(threadIdx.x_2 + 40)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv((threadIdx.x_2 + 16), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3)) + 4608)]
+              attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 8;
+              kernel.shared_1[(threadIdx.x_2 + 48)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv(threadIdx.x_2, 3)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3)) + 9216)]
+              attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 8;
+              kernel.shared_1[(threadIdx.x_2 + 56)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv((threadIdx.x_2 + 8), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3)) + 9216)]
+              attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 8;
+              kernel.shared_1[(threadIdx.x_2 + 64)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv((threadIdx.x_2 + 16), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3)) + 9216)]
+              attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 8;
+              kernel.shared_1[(threadIdx.x_2 + 72)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv(threadIdx.x_2, 3)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3)) + 13824)]
+              attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 8;
+              kernel.shared_1[(threadIdx.x_2 + 80)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv((threadIdx.x_2 + 8), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3)) + 13824)]
+              attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 8;
+              kernel.shared_1[(threadIdx.x_2 + 88)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv((threadIdx.x_2 + 16), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3)) + 13824)]
+              attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 8;
+              kernel.shared_1[(threadIdx.x_2 + 96)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv(threadIdx.x_2, 3)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3)) + 18432)]
+              attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 8;
+              kernel.shared_1[(threadIdx.x_2 + 104)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv((threadIdx.x_2 + 8), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3)) + 18432)]
+              attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 8;
+              kernel.shared_1[(threadIdx.x_2 + 112)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv((threadIdx.x_2 + 16), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3)) + 18432)]
+              attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 8;
+              kernel.shared_1[(threadIdx.x_2 + 120)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv(threadIdx.x_2, 3)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3)) + 23040)]
+              attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 8;
+              kernel.shared_1[(threadIdx.x_2 + 128)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv((threadIdx.x_2 + 8), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3)) + 23040)]
+              attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 8;
+              kernel.shared_1[(threadIdx.x_2 + 136)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv((threadIdx.x_2 + 16), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3)) + 23040)]
+              attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 8;
+              kernel.shared_1[(threadIdx.x_2 + 144)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv(threadIdx.x_2, 3)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3)) + 27648)]
+              attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 8;
+              kernel.shared_1[(threadIdx.x_2 + 152)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv((threadIdx.x_2 + 8), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3)) + 27648)]
+              attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 8;
+              kernel.shared_1[(threadIdx.x_2 + 160)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv((threadIdx.x_2 + 16), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3)) + 27648)]
+              attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 8;
+              kernel.shared_1[(threadIdx.x_2 + 168)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv(threadIdx.x_2, 3)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3)) + 32256)]
+              attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 8;
+              kernel.shared_1[(threadIdx.x_2 + 176)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv((threadIdx.x_2 + 8), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3)) + 32256)]
+              attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 8;
+              kernel.shared_1[(threadIdx.x_2 + 184)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv((threadIdx.x_2 + 16), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3)) + 32256)]
+              attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 8;
+              kernel.shared_1[(threadIdx.x_2 + 192)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv(threadIdx.x_2, 3)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3)) + 36864)]
+              attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 8;
+              kernel.shared_1[(threadIdx.x_2 + 200)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv((threadIdx.x_2 + 8), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3)) + 36864)]
+              attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 8;
+              kernel.shared_1[(threadIdx.x_2 + 208)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv((threadIdx.x_2 + 16), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3)) + 36864)]
+              attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 8;
+              kernel.shared_1[(threadIdx.x_2 + 216)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv(threadIdx.x_2, 3)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3)) + 41472)]
+              attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 8;
+              kernel.shared_1[(threadIdx.x_2 + 224)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv((threadIdx.x_2 + 8), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3)) + 41472)]
+              attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 8;
+              kernel.shared_1[(threadIdx.x_2 + 232)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv((threadIdx.x_2 + 16), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3)) + 41472)]
+              attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 8;
+              kernel.shared_1[(threadIdx.x_2 + 240)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv(threadIdx.x_2, 3)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3)) + 46080)]
+              attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 8;
+              kernel.shared_1[(threadIdx.x_2 + 248)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv((threadIdx.x_2 + 8), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3)) + 46080)]
+              attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 8;
+              kernel.shared_1[(threadIdx.x_2 + 256)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv((threadIdx.x_2 + 16), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3)) + 46080)]
+              attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 8;
+              kernel.shared_1[(threadIdx.x_2 + 264)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv(threadIdx.x_2, 3)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3)) + 50688)]
+              attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 8;
+              kernel.shared_1[(threadIdx.x_2 + 272)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv((threadIdx.x_2 + 8), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3)) + 50688)]
+              attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 8;
+              kernel.shared_1[(threadIdx.x_2 + 280)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv((threadIdx.x_2 + 16), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3)) + 50688)]
+              attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 8;
+              kernel.shared_1[(threadIdx.x_2 + 288)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv(threadIdx.x_2, 3)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3)) + 55296)]
+              attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 8;
+              kernel.shared_1[(threadIdx.x_2 + 296)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv((threadIdx.x_2 + 8), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3)) + 55296)]
+              attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 8;
+              kernel.shared_1[(threadIdx.x_2 + 304)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv((threadIdx.x_2 + 16), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3)) + 55296)]
+              attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 8;
+              kernel.shared_1[(threadIdx.x_2 + 312)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv(threadIdx.x_2, 3)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3)) + 59904)]
+              attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 8;
+              kernel.shared_1[(threadIdx.x_2 + 320)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv((threadIdx.x_2 + 8), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3)) + 59904)]
+              attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 8;
+              kernel.shared_1[(threadIdx.x_2 + 328)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv((threadIdx.x_2 + 16), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3)) + 59904)]
+              attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 8;
+              kernel.shared_1[(threadIdx.x_2 + 336)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv(threadIdx.x_2, 3)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3)) + 64512)]
+              attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 8;
+              kernel.shared_1[(threadIdx.x_2 + 344)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv((threadIdx.x_2 + 8), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3)) + 64512)]
+              attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 8;
+              kernel.shared_1[(threadIdx.x_2 + 352)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv((threadIdx.x_2 + 16), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3)) + 64512)]
+              attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 8;
+              kernel.shared_1[(threadIdx.x_2 + 360)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv(threadIdx.x_2, 3)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3)) + 69120)]
+              attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 8;
+              kernel.shared_1[(threadIdx.x_2 + 368)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv((threadIdx.x_2 + 8), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3)) + 69120)]
+              attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 8;
+              kernel.shared_1[(threadIdx.x_2 + 376)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv((threadIdx.x_2 + 16), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3)) + 69120)]
+              conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[0]*kernel.shared_1[(threadIdx.x*24)]))
+              conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[1]*kernel.shared_1[(threadIdx.x*24)]))
+              conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[2]*kernel.shared_1[(threadIdx.x*24)]))
+              conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[3]*kernel.shared_1[(threadIdx.x*24)]))
+              conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[4]*kernel.shared_1[(threadIdx.x*24)]))
+              conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[5]*kernel.shared_1[(threadIdx.x*24)]))
+              conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[6]*kernel.shared_1[(threadIdx.x*24)]))
+              conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[0]*kernel.shared_1[((threadIdx.x*24) + 192)]))
+              conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[1]*kernel.shared_1[((threadIdx.x*24) + 192)]))
+              conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[2]*kernel.shared_1[((threadIdx.x*24) + 192)]))
+              conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[3]*kernel.shared_1[((threadIdx.x*24) + 192)]))
+              conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[4]*kernel.shared_1[((threadIdx.x*24) + 192)]))
+              conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[5]*kernel.shared_1[((threadIdx.x*24) + 192)]))
+              conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[6]*kernel.shared_1[((threadIdx.x*24) + 192)]))
+              conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[1]*kernel.shared_1[((threadIdx.x*24) + 1)]))
+              conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[2]*kernel.shared_1[((threadIdx.x*24) + 1)]))
+              conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[3]*kernel.shared_1[((threadIdx.x*24) + 1)]))
+              conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[4]*kernel.shared_1[((threadIdx.x*24) + 1)]))
+              conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[5]*kernel.shared_1[((threadIdx.x*24) + 1)]))
+              conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[6]*kernel.shared_1[((threadIdx.x*24) + 1)]))
+              conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[7]*kernel.shared_1[((threadIdx.x*24) + 1)]))
+              conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[1]*kernel.shared_1[((threadIdx.x*24) + 193)]))
+              conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[2]*kernel.shared_1[((threadIdx.x*24) + 193)]))
+              conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[3]*kernel.shared_1[((threadIdx.x*24) + 193)]))
+              conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[4]*kernel.shared_1[((threadIdx.x*24) + 193)]))
+              conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[5]*kernel.shared_1[((threadIdx.x*24) + 193)]))
+              conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[6]*kernel.shared_1[((threadIdx.x*24) + 193)]))
+              conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[7]*kernel.shared_1[((threadIdx.x*24) + 193)]))
+              conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[2]*kernel.shared_1[((threadIdx.x*24) + 2)]))
+              conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[3]*kernel.shared_1[((threadIdx.x*24) + 2)]))
+              conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[4]*kernel.shared_1[((threadIdx.x*24) + 2)]))
+              conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[5]*kernel.shared_1[((threadIdx.x*24) + 2)]))
+              conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[6]*kernel.shared_1[((threadIdx.x*24) + 2)]))
+              conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[7]*kernel.shared_1[((threadIdx.x*24) + 2)]))
+              conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[8]*kernel.shared_1[((threadIdx.x*24) + 2)]))
+              conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[2]*kernel.shared_1[((threadIdx.x*24) + 194)]))
+              conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[3]*kernel.shared_1[((threadIdx.x*24) + 194)]))
+              conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[4]*kernel.shared_1[((threadIdx.x*24) + 194)]))
+              conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[5]*kernel.shared_1[((threadIdx.x*24) + 194)]))
+              conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[6]*kernel.shared_1[((threadIdx.x*24) + 194)]))
+              conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[7]*kernel.shared_1[((threadIdx.x*24) + 194)]))
+              conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[8]*kernel.shared_1[((threadIdx.x*24) + 194)]))
+              conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[9]*kernel.shared_1[((threadIdx.x*24) + 3)]))
+              conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[10]*kernel.shared_1[((threadIdx.x*24) + 3)]))
+              conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[11]*kernel.shared_1[((threadIdx.x*24) + 3)]))
+              conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[12]*kernel.shared_1[((threadIdx.x*24) + 3)]))
+              conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[13]*kernel.shared_1[((threadIdx.x*24) + 3)]))
+              conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[14]*kernel.shared_1[((threadIdx.x*24) + 3)]))
+              conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[15]*kernel.shared_1[((threadIdx.x*24) + 3)]))
+              conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[9]*kernel.shared_1[((threadIdx.x*24) + 195)]))
+              conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[10]*kernel.shared_1[((threadIdx.x*24) + 195)]))
+              conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[11]*kernel.shared_1[((threadIdx.x*24) + 195)]))
+              conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[12]*kernel.shared_1[((threadIdx.x*24) + 195)]))
+              conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[13]*kernel.shared_1[((threadIdx.x*24) + 195)]))
+              conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[14]*kernel.shared_1[((threadIdx.x*24) + 195)]))
+              conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[15]*kernel.shared_1[((threadIdx.x*24) + 195)]))
+              conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[10]*kernel.shared_1[((threadIdx.x*24) + 4)]))
+              conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[11]*kernel.shared_1[((threadIdx.x*24) + 4)]))
+              conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[12]*kernel.shared_1[((threadIdx.x*24) + 4)]))
+              conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[13]*kernel.shared_1[((threadIdx.x*24) + 4)]))
+              conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[14]*kernel.shared_1[((threadIdx.x*24) + 4)]))
+              conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[15]*kernel.shared_1[((threadIdx.x*24) + 4)]))
+              conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[16]*kernel.shared_1[((threadIdx.x*24) + 4)]))
+              conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[10]*kernel.shared_1[((threadIdx.x*24) + 196)]))
+              conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[11]*kernel.shared_1[((threadIdx.x*24) + 196)]))
+              conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[12]*kernel.shared_1[((threadIdx.x*24) + 196)]))
+              conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[13]*kernel.shared_1[((threadIdx.x*24) + 196)]))
+              conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[14]*kernel.shared_1[((threadIdx.x*24) + 196)]))
+              conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[15]*kernel.shared_1[((threadIdx.x*24) + 196)]))
+              conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[16]*kernel.shared_1[((threadIdx.x*24) + 196)]))
+              conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[11]*kernel.shared_1[((threadIdx.x*24) + 5)]))
+              conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[12]*kernel.shared_1[((threadIdx.x*24) + 5)]))
+              conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[13]*kernel.shared_1[((threadIdx.x*24) + 5)]))
+              conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[14]*kernel.shared_1[((threadIdx.x*24) + 5)]))
+              conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[15]*kernel.shared_1[((threadIdx.x*24) + 5)]))
+              conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[16]*kernel.shared_1[((threadIdx.x*24) + 5)]))
+              conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[17]*kernel.shared_1[((threadIdx.x*24) + 5)]))
+              conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[11]*kernel.shared_1[((threadIdx.x*24) + 197)]))
+              conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[12]*kernel.shared_1[((threadIdx.x*24) + 197)]))
+              conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[13]*kernel.shared_1[((threadIdx.x*24) + 197)]))
+              conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[14]*kernel.shared_1[((threadIdx.x*24) + 197)]))
+              conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[15]*kernel.shared_1[((threadIdx.x*24) + 197)]))
+              conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[16]*kernel.shared_1[((threadIdx.x*24) + 197)]))
+              conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[17]*kernel.shared_1[((threadIdx.x*24) + 197)]))
+              conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[18]*kernel.shared_1[((threadIdx.x*24) + 6)]))
+              conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[19]*kernel.shared_1[((threadIdx.x*24) + 6)]))
+              conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[20]*kernel.shared_1[((threadIdx.x*24) + 6)]))
+              conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[21]*kernel.shared_1[((threadIdx.x*24) + 6)]))
+              conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[22]*kernel.shared_1[((threadIdx.x*24) + 6)]))
+              conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[23]*kernel.shared_1[((threadIdx.x*24) + 6)]))
+              conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[24]*kernel.shared_1[((threadIdx.x*24) + 6)]))
+              conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[18]*kernel.shared_1[((threadIdx.x*24) + 198)]))
+              conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[19]*kernel.shared_1[((threadIdx.x*24) + 198)]))
+              conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[20]*kernel.shared_1[((threadIdx.x*24) + 198)]))
+              conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[21]*kernel.shared_1[((threadIdx.x*24) + 198)]))
+              conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[22]*kernel.shared_1[((threadIdx.x*24) + 198)]))
+              conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[23]*kernel.shared_1[((threadIdx.x*24) + 198)]))
+              conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[24]*kernel.shared_1[((threadIdx.x*24) + 198)]))
+              conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[19]*kernel.shared_1[((threadIdx.x*24) + 7)]))
+              conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[20]*kernel.shared_1[((threadIdx.x*24) + 7)]))
+              conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[21]*kernel.shared_1[((threadIdx.x*24) + 7)]))
+              conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[22]*kernel.shared_1[((threadIdx.x*24) + 7)]))
+              conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[23]*kernel.shared_1[((threadIdx.x*24) + 7)]))
+              conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[24]*kernel.shared_1[((threadIdx.x*24) + 7)]))
+              conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[25]*kernel.shared_1[((threadIdx.x*24) + 7)]))
+              conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[19]*kernel.shared_1[((threadIdx.x*24) + 199)]))
+              conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[20]*kernel.shared_1[((threadIdx.x*24) + 199)]))
+              conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[21]*kernel.shared_1[((threadIdx.x*24) + 199)]))
+              conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[22]*kernel.shared_1[((threadIdx.x*24) + 199)]))
+              conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[23]*kernel.shared_1[((threadIdx.x*24) + 199)]))
+              conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[24]*kernel.shared_1[((threadIdx.x*24) + 199)]))
+              conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[25]*kernel.shared_1[((threadIdx.x*24) + 199)]))
+              conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[20]*kernel.shared_1[((threadIdx.x*24) + 8)]))
+              conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[21]*kernel.shared_1[((threadIdx.x*24) + 8)]))
+              conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[22]*kernel.shared_1[((threadIdx.x*24) + 8)]))
+              conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[23]*kernel.shared_1[((threadIdx.x*24) + 8)]))
+              conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[24]*kernel.shared_1[((threadIdx.x*24) + 8)]))
+              conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[25]*kernel.shared_1[((threadIdx.x*24) + 8)]))
+              conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[26]*kernel.shared_1[((threadIdx.x*24) + 8)]))
+              conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[20]*kernel.shared_1[((threadIdx.x*24) + 200)]))
+              conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[21]*kernel.shared_1[((threadIdx.x*24) + 200)]))
+              conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[22]*kernel.shared_1[((threadIdx.x*24) + 200)]))
+              conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[23]*kernel.shared_1[((threadIdx.x*24) + 200)]))
+              conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[24]*kernel.shared_1[((threadIdx.x*24) + 200)]))
+              conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[25]*kernel.shared_1[((threadIdx.x*24) + 200)]))
+              conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[26]*kernel.shared_1[((threadIdx.x*24) + 200)]))
+              conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[27]*kernel.shared_1[((threadIdx.x*24) + 9)]))
+              conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[28]*kernel.shared_1[((threadIdx.x*24) + 9)]))
+              conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[29]*kernel.shared_1[((threadIdx.x*24) + 9)]))
+              conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[30]*kernel.shared_1[((threadIdx.x*24) + 9)]))
+              conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[31]*kernel.shared_1[((threadIdx.x*24) + 9)]))
+              conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[32]*kernel.shared_1[((threadIdx.x*24) + 9)]))
+              conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[33]*kernel.shared_1[((threadIdx.x*24) + 9)]))
+              conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[27]*kernel.shared_1[((threadIdx.x*24) + 201)]))
+              conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[28]*kernel.shared_1[((threadIdx.x*24) + 201)]))
+              conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[29]*kernel.shared_1[((threadIdx.x*24) + 201)]))
+              conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[30]*kernel.shared_1[((threadIdx.x*24) + 201)]))
+              conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[31]*kernel.shared_1[((threadIdx.x*24) + 201)]))
+              conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[32]*kernel.shared_1[((threadIdx.x*24) + 201)]))
+              conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[33]*kernel.shared_1[((threadIdx.x*24) + 201)]))
+              conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[28]*kernel.shared_1[((threadIdx.x*24) + 10)]))
+              conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[29]*kernel.shared_1[((threadIdx.x*24) + 10)]))
+              conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[30]*kernel.shared_1[((threadIdx.x*24) + 10)]))
+              conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[31]*kernel.shared_1[((threadIdx.x*24) + 10)]))
+              conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[32]*kernel.shared_1[((threadIdx.x*24) + 10)]))
+              conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[33]*kernel.shared_1[((threadIdx.x*24) + 10)]))
+              conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[34]*kernel.shared_1[((threadIdx.x*24) + 10)]))
+              conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[28]*kernel.shared_1[((threadIdx.x*24) + 202)]))
+              conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[29]*kernel.shared_1[((threadIdx.x*24) + 202)]))
+              conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[30]*kernel.shared_1[((threadIdx.x*24) + 202)]))
+              conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[31]*kernel.shared_1[((threadIdx.x*24) + 202)]))
+              conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[32]*kernel.shared_1[((threadIdx.x*24) + 202)]))
+              conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[33]*kernel.shared_1[((threadIdx.x*24) + 202)]))
+              conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[34]*kernel.shared_1[((threadIdx.x*24) + 202)]))
+              conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[29]*kernel.shared_1[((threadIdx.x*24) + 11)]))
+              conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[30]*kernel.shared_1[((threadIdx.x*24) + 11)]))
+              conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[31]*kernel.shared_1[((threadIdx.x*24) + 11)]))
+              conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[32]*kernel.shared_1[((threadIdx.x*24) + 11)]))
+              conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[33]*kernel.shared_1[((threadIdx.x*24) + 11)]))
+              conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[34]*kernel.shared_1[((threadIdx.x*24) + 11)]))
+              conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[35]*kernel.shared_1[((threadIdx.x*24) + 11)]))
+              conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[29]*kernel.shared_1[((threadIdx.x*24) + 203)]))
+              conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[30]*kernel.shared_1[((threadIdx.x*24) + 203)]))
+              conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[31]*kernel.shared_1[((threadIdx.x*24) + 203)]))
+              conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[32]*kernel.shared_1[((threadIdx.x*24) + 203)]))
+              conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[33]*kernel.shared_1[((threadIdx.x*24) + 203)]))
+              conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[34]*kernel.shared_1[((threadIdx.x*24) + 203)]))
+              conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[35]*kernel.shared_1[((threadIdx.x*24) + 203)]))
+              conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[36]*kernel.shared_1[((threadIdx.x*24) + 12)]))
+              conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[37]*kernel.shared_1[((threadIdx.x*24) + 12)]))
+              conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[38]*kernel.shared_1[((threadIdx.x*24) + 12)]))
+              conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[39]*kernel.shared_1[((threadIdx.x*24) + 12)]))
+              conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[40]*kernel.shared_1[((threadIdx.x*24) + 12)]))
+              conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[41]*kernel.shared_1[((threadIdx.x*24) + 12)]))
+              conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[42]*kernel.shared_1[((threadIdx.x*24) + 12)]))
+              conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[36]*kernel.shared_1[((threadIdx.x*24) + 204)]))
+              conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[37]*kernel.shared_1[((threadIdx.x*24) + 204)]))
+              conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[38]*kernel.shared_1[((threadIdx.x*24) + 204)]))
+              conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[39]*kernel.shared_1[((threadIdx.x*24) + 204)]))
+              conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[40]*kernel.shared_1[((threadIdx.x*24) + 204)]))
+              conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[41]*kernel.shared_1[((threadIdx.x*24) + 204)]))
+              conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[42]*kernel.shared_1[((threadIdx.x*24) + 204)]))
+              conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[37]*kernel.shared_1[((threadIdx.x*24) + 13)]))
+              conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[38]*kernel.shared_1[((threadIdx.x*24) + 13)]))
+              conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[39]*kernel.shared_1[((threadIdx.x*24) + 13)]))
+              conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[40]*kernel.shared_1[((threadIdx.x*24) + 13)]))
+              conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[41]*kernel.shared_1[((threadIdx.x*24) + 13)]))
+              conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[42]*kernel.shared_1[((threadIdx.x*24) + 13)]))
+              conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[43]*kernel.shared_1[((threadIdx.x*24) + 13)]))
+              conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[37]*kernel.shared_1[((threadIdx.x*24) + 205)]))
+              conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[38]*kernel.shared_1[((threadIdx.x*24) + 205)]))
+              conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[39]*kernel.shared_1[((threadIdx.x*24) + 205)]))
+              conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[40]*kernel.shared_1[((threadIdx.x*24) + 205)]))
+              conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[41]*kernel.shared_1[((threadIdx.x*24) + 205)]))
+              conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[42]*kernel.shared_1[((threadIdx.x*24) + 205)]))
+              conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[43]*kernel.shared_1[((threadIdx.x*24) + 205)]))
+              conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[38]*kernel.shared_1[((threadIdx.x*24) + 14)]))
+              conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[39]*kernel.shared_1[((threadIdx.x*24) + 14)]))
+              conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[40]*kernel.shared_1[((threadIdx.x*24) + 14)]))
+              conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[41]*kernel.shared_1[((threadIdx.x*24) + 14)]))
+              conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[42]*kernel.shared_1[((threadIdx.x*24) + 14)]))
+              conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[43]*kernel.shared_1[((threadIdx.x*24) + 14)]))
+              conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[44]*kernel.shared_1[((threadIdx.x*24) + 14)]))
+              conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[38]*kernel.shared_1[((threadIdx.x*24) + 206)]))
+              conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[39]*kernel.shared_1[((threadIdx.x*24) + 206)]))
+              conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[40]*kernel.shared_1[((threadIdx.x*24) + 206)]))
+              conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[41]*kernel.shared_1[((threadIdx.x*24) + 206)]))
+              conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[42]*kernel.shared_1[((threadIdx.x*24) + 206)]))
+              conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[43]*kernel.shared_1[((threadIdx.x*24) + 206)]))
+              conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[44]*kernel.shared_1[((threadIdx.x*24) + 206)]))
+              conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[45]*kernel.shared_1[((threadIdx.x*24) + 15)]))
+              conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[46]*kernel.shared_1[((threadIdx.x*24) + 15)]))
+              conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[47]*kernel.shared_1[((threadIdx.x*24) + 15)]))
+              conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[48]*kernel.shared_1[((threadIdx.x*24) + 15)]))
+              conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[49]*kernel.shared_1[((threadIdx.x*24) + 15)]))
+              conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[50]*kernel.shared_1[((threadIdx.x*24) + 15)]))
+              conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[51]*kernel.shared_1[((threadIdx.x*24) + 15)]))
+              conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[45]*kernel.shared_1[((threadIdx.x*24) + 207)]))
+              conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[46]*kernel.shared_1[((threadIdx.x*24) + 207)]))
+              conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[47]*kernel.shared_1[((threadIdx.x*24) + 207)]))
+              conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[48]*kernel.shared_1[((threadIdx.x*24) + 207)]))
+              conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[49]*kernel.shared_1[((threadIdx.x*24) + 207)]))
+              conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[50]*kernel.shared_1[((threadIdx.x*24) + 207)]))
+              conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[51]*kernel.shared_1[((threadIdx.x*24) + 207)]))
+              conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[46]*kernel.shared_1[((threadIdx.x*24) + 16)]))
+              conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[47]*kernel.shared_1[((threadIdx.x*24) + 16)]))
+              conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[48]*kernel.shared_1[((threadIdx.x*24) + 16)]))
+              conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[49]*kernel.shared_1[((threadIdx.x*24) + 16)]))
+              conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[50]*kernel.shared_1[((threadIdx.x*24) + 16)]))
+              conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[51]*kernel.shared_1[((threadIdx.x*24) + 16)]))
+              conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[52]*kernel.shared_1[((threadIdx.x*24) + 16)]))
+              conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[46]*kernel.shared_1[((threadIdx.x*24) + 208)]))
+              conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[47]*kernel.shared_1[((threadIdx.x*24) + 208)]))
+              conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[48]*kernel.shared_1[((threadIdx.x*24) + 208)]))
+              conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[49]*kernel.shared_1[((threadIdx.x*24) + 208)]))
+              conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[50]*kernel.shared_1[((threadIdx.x*24) + 208)]))
+              conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[51]*kernel.shared_1[((threadIdx.x*24) + 208)]))
+              conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[52]*kernel.shared_1[((threadIdx.x*24) + 208)]))
+              conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[47]*kernel.shared_1[((threadIdx.x*24) + 17)]))
+              conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[48]*kernel.shared_1[((threadIdx.x*24) + 17)]))
+              conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[49]*kernel.shared_1[((threadIdx.x*24) + 17)]))
+              conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[50]*kernel.shared_1[((threadIdx.x*24) + 17)]))
+              conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[51]*kernel.shared_1[((threadIdx.x*24) + 17)]))
+              conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[52]*kernel.shared_1[((threadIdx.x*24) + 17)]))
+              conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[53]*kernel.shared_1[((threadIdx.x*24) + 17)]))
+              conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[47]*kernel.shared_1[((threadIdx.x*24) + 209)]))
+              conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[48]*kernel.shared_1[((threadIdx.x*24) + 209)]))
+              conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[49]*kernel.shared_1[((threadIdx.x*24) + 209)]))
+              conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[50]*kernel.shared_1[((threadIdx.x*24) + 209)]))
+              conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[51]*kernel.shared_1[((threadIdx.x*24) + 209)]))
+              conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[52]*kernel.shared_1[((threadIdx.x*24) + 209)]))
+              conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[53]*kernel.shared_1[((threadIdx.x*24) + 209)]))
+              conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[54]*kernel.shared_1[((threadIdx.x*24) + 18)]))
+              conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[55]*kernel.shared_1[((threadIdx.x*24) + 18)]))
+              conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[56]*kernel.shared_1[((threadIdx.x*24) + 18)]))
+              conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[57]*kernel.shared_1[((threadIdx.x*24) + 18)]))
+              conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[58]*kernel.shared_1[((threadIdx.x*24) + 18)]))
+              conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[59]*kernel.shared_1[((threadIdx.x*24) + 18)]))
+              conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[60]*kernel.shared_1[((threadIdx.x*24) + 18)]))
+              conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[54]*kernel.shared_1[((threadIdx.x*24) + 210)]))
+              conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[55]*kernel.shared_1[((threadIdx.x*24) + 210)]))
+              conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[56]*kernel.shared_1[((threadIdx.x*24) + 210)]))
+              conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[57]*kernel.shared_1[((threadIdx.x*24) + 210)]))
+              conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[58]*kernel.shared_1[((threadIdx.x*24) + 210)]))
+              conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[59]*kernel.shared_1[((threadIdx.x*24) + 210)]))
+              conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[60]*kernel.shared_1[((threadIdx.x*24) + 210)]))
+              conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[55]*kernel.shared_1[((threadIdx.x*24) + 19)]))
+              conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[56]*kernel.shared_1[((threadIdx.x*24) + 19)]))
+              conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[57]*kernel.shared_1[((threadIdx.x*24) + 19)]))
+              conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[58]*kernel.shared_1[((threadIdx.x*24) + 19)]))
+              conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[59]*kernel.shared_1[((threadIdx.x*24) + 19)]))
+              conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[60]*kernel.shared_1[((threadIdx.x*24) + 19)]))
+              conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[61]*kernel.shared_1[((threadIdx.x*24) + 19)]))
+              conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[55]*kernel.shared_1[((threadIdx.x*24) + 211)]))
+              conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[56]*kernel.shared_1[((threadIdx.x*24) + 211)]))
+              conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[57]*kernel.shared_1[((threadIdx.x*24) + 211)]))
+              conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[58]*kernel.shared_1[((threadIdx.x*24) + 211)]))
+              conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[59]*kernel.shared_1[((threadIdx.x*24) + 211)]))
+              conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[60]*kernel.shared_1[((threadIdx.x*24) + 211)]))
+              conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[61]*kernel.shared_1[((threadIdx.x*24) + 211)]))
+              conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[56]*kernel.shared_1[((threadIdx.x*24) + 20)]))
+              conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[57]*kernel.shared_1[((threadIdx.x*24) + 20)]))
+              conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[58]*kernel.shared_1[((threadIdx.x*24) + 20)]))
+              conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[59]*kernel.shared_1[((threadIdx.x*24) + 20)]))
+              conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[60]*kernel.shared_1[((threadIdx.x*24) + 20)]))
+              conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[61]*kernel.shared_1[((threadIdx.x*24) + 20)]))
+              conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[62]*kernel.shared_1[((threadIdx.x*24) + 20)]))
+              conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[56]*kernel.shared_1[((threadIdx.x*24) + 212)]))
+              conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[57]*kernel.shared_1[((threadIdx.x*24) + 212)]))
+              conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[58]*kernel.shared_1[((threadIdx.x*24) + 212)]))
+              conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[59]*kernel.shared_1[((threadIdx.x*24) + 212)]))
+              conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[60]*kernel.shared_1[((threadIdx.x*24) + 212)]))
+              conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[61]*kernel.shared_1[((threadIdx.x*24) + 212)]))
+              conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[62]*kernel.shared_1[((threadIdx.x*24) + 212)]))
+              conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[63]*kernel.shared_1[((threadIdx.x*24) + 21)]))
+              conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[64]*kernel.shared_1[((threadIdx.x*24) + 21)]))
+              conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[65]*kernel.shared_1[((threadIdx.x*24) + 21)]))
+              conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[66]*kernel.shared_1[((threadIdx.x*24) + 21)]))
+              conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[67]*kernel.shared_1[((threadIdx.x*24) + 21)]))
+              conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[68]*kernel.shared_1[((threadIdx.x*24) + 21)]))
+              conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[69]*kernel.shared_1[((threadIdx.x*24) + 21)]))
+              conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[63]*kernel.shared_1[((threadIdx.x*24) + 213)]))
+              conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[64]*kernel.shared_1[((threadIdx.x*24) + 213)]))
+              conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[65]*kernel.shared_1[((threadIdx.x*24) + 213)]))
+              conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[66]*kernel.shared_1[((threadIdx.x*24) + 213)]))
+              conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[67]*kernel.shared_1[((threadIdx.x*24) + 213)]))
+              conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[68]*kernel.shared_1[((threadIdx.x*24) + 213)]))
+              conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[69]*kernel.shared_1[((threadIdx.x*24) + 213)]))
+              conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[64]*kernel.shared_1[((threadIdx.x*24) + 22)]))
+              conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[65]*kernel.shared_1[((threadIdx.x*24) + 22)]))
+              conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[66]*kernel.shared_1[((threadIdx.x*24) + 22)]))
+              conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[67]*kernel.shared_1[((threadIdx.x*24) + 22)]))
+              conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[68]*kernel.shared_1[((threadIdx.x*24) + 22)]))
+              conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[69]*kernel.shared_1[((threadIdx.x*24) + 22)]))
+              conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[70]*kernel.shared_1[((threadIdx.x*24) + 22)]))
+              conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[64]*kernel.shared_1[((threadIdx.x*24) + 214)]))
+              conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[65]*kernel.shared_1[((threadIdx.x*24) + 214)]))
+              conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[66]*kernel.shared_1[((threadIdx.x*24) + 214)]))
+              conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[67]*kernel.shared_1[((threadIdx.x*24) + 214)]))
+              conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[68]*kernel.shared_1[((threadIdx.x*24) + 214)]))
+              conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[69]*kernel.shared_1[((threadIdx.x*24) + 214)]))
+              conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[70]*kernel.shared_1[((threadIdx.x*24) + 214)]))
+              conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[65]*kernel.shared_1[((threadIdx.x*24) + 23)]))
+              conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[66]*kernel.shared_1[((threadIdx.x*24) + 23)]))
+              conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[67]*kernel.shared_1[((threadIdx.x*24) + 23)]))
+              conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[68]*kernel.shared_1[((threadIdx.x*24) + 23)]))
+              conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[69]*kernel.shared_1[((threadIdx.x*24) + 23)]))
+              conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[70]*kernel.shared_1[((threadIdx.x*24) + 23)]))
+              conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[71]*kernel.shared_1[((threadIdx.x*24) + 23)]))
+              conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[65]*kernel.shared_1[((threadIdx.x*24) + 215)]))
+              conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[66]*kernel.shared_1[((threadIdx.x*24) + 215)]))
+              conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[67]*kernel.shared_1[((threadIdx.x*24) + 215)]))
+              conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[68]*kernel.shared_1[((threadIdx.x*24) + 215)]))
+              conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[69]*kernel.shared_1[((threadIdx.x*24) + 215)]))
+              conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[70]*kernel.shared_1[((threadIdx.x*24) + 215)]))
+              conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[71]*kernel.shared_1[((threadIdx.x*24) + 215)]))
             }
           }
         }
-        for (i1.inner: int32, 0, 4) {
-          compute[((((blockIdx.x*784) + (floordiv(threadIdx.x, 7)*196)) + (i1.inner*49)) + floormod(threadIdx.x, 7))] = max((conv2d_nchw_1[i1.inner] + bias[(((blockIdx.x*16) + (floordiv(threadIdx.x, 7)*4)) + i1.inner)]), 0f32)
-          compute[(((((blockIdx.x*784) + (floordiv(threadIdx.x, 7)*196)) + (i1.inner*49)) + floormod(threadIdx.x, 7)) + 7)] = max((conv2d_nchw_1[(i1.inner + 4)] + bias[(((blockIdx.x*16) + (floordiv(threadIdx.x, 7)*4)) + i1.inner)]), 0f32)
-          compute[(((((blockIdx.x*784) + (floordiv(threadIdx.x, 7)*196)) + (i1.inner*49)) + floormod(threadIdx.x, 7)) + 14)] = max((conv2d_nchw_1[(i1.inner + 8)] + bias[(((blockIdx.x*16) + (floordiv(threadIdx.x, 7)*4)) + i1.inner)]), 0f32)
-          compute[(((((blockIdx.x*784) + (floordiv(threadIdx.x, 7)*196)) + (i1.inner*49)) + floormod(threadIdx.x, 7)) + 21)] = max((conv2d_nchw_1[(i1.inner + 12)] + bias[(((blockIdx.x*16) + (floordiv(threadIdx.x, 7)*4)) + i1.inner)]), 0f32)
-          compute[(((((blockIdx.x*784) + (floordiv(threadIdx.x, 7)*196)) + (i1.inner*49)) + floormod(threadIdx.x, 7)) + 28)] = max((conv2d_nchw_1[(i1.inner + 16)] + bias[(((blockIdx.x*16) + (floordiv(threadIdx.x, 7)*4)) + i1.inner)]), 0f32)
-          compute[(((((blockIdx.x*784) + (floordiv(threadIdx.x, 7)*196)) + (i1.inner*49)) + floormod(threadIdx.x, 7)) + 35)] = max((conv2d_nchw_1[(i1.inner + 20)] + bias[(((blockIdx.x*16) + (floordiv(threadIdx.x, 7)*4)) + i1.inner)]), 0f32)
-          compute[(((((blockIdx.x*784) + (floordiv(threadIdx.x, 7)*196)) + (i1.inner*49)) + floormod(threadIdx.x, 7)) + 42)] = max((conv2d_nchw_1[(i1.inner + 24)] + bias[(((blockIdx.x*16) + (floordiv(threadIdx.x, 7)*4)) + i1.inner)]), 0f32)
-        }
+        compute[(((floordiv(blockIdx.x, 7)*784) + (threadIdx.x*49)) + (floormod(blockIdx.x, 7)*7))] = max((conv2d_nchw_1[0] + bias[((floordiv(blockIdx.x, 7)*16) + threadIdx.x)]), 0f32)
+        compute[((((floordiv(blockIdx.x, 7)*784) + (threadIdx.x*49)) + (floormod(blockIdx.x, 7)*7)) + 1)] = max((conv2d_nchw_1[1] + bias[((floordiv(blockIdx.x, 7)*16) + threadIdx.x)]), 0f32)
+        compute[((((floordiv(blockIdx.x, 7)*784) + (threadIdx.x*49)) + (floormod(blockIdx.x, 7)*7)) + 2)] = max((conv2d_nchw_1[2] + bias[((floordiv(blockIdx.x, 7)*16) + threadIdx.x)]), 0f32)
+        compute[((((floordiv(blockIdx.x, 7)*784) + (threadIdx.x*49)) + (floormod(blockIdx.x, 7)*7)) + 3)] = max((conv2d_nchw_1[3] + bias[((floordiv(blockIdx.x, 7)*16) + threadIdx.x)]), 0f32)
+        compute[((((floordiv(blockIdx.x, 7)*784) + (threadIdx.x*49)) + (floormod(blockIdx.x, 7)*7)) + 4)] = max((conv2d_nchw_1[4] + bias[((floordiv(blockIdx.x, 7)*16) + threadIdx.x)]), 0f32)
+        compute[((((floordiv(blockIdx.x, 7)*784) + (threadIdx.x*49)) + (floormod(blockIdx.x, 7)*7)) + 5)] = max((conv2d_nchw_1[5] + bias[((floordiv(blockIdx.x, 7)*16) + threadIdx.x)]), 0f32)
+        compute[((((floordiv(blockIdx.x, 7)*784) + (threadIdx.x*49)) + (floormod(blockIdx.x, 7)*7)) + 6)] = max((conv2d_nchw_1[6] + bias[((floordiv(blockIdx.x, 7)*16) + threadIdx.x)]), 0f32)
+        compute[((((floordiv(blockIdx.x, 7)*784) + (threadIdx.x*49)) + (floormod(blockIdx.x, 7)*7)) + 392)] = max((conv2d_nchw_1[7] + bias[(((floordiv(blockIdx.x, 7)*16) + threadIdx.x) + 8)]), 0f32)
+        compute[((((floordiv(blockIdx.x, 7)*784) + (threadIdx.x*49)) + (floormod(blockIdx.x, 7)*7)) + 393)] = max((conv2d_nchw_1[8] + bias[(((floordiv(blockIdx.x, 7)*16) + threadIdx.x) + 8)]), 0f32)
+        compute[((((floordiv(blockIdx.x, 7)*784) + (threadIdx.x*49)) + (floormod(blockIdx.x, 7)*7)) + 394)] = max((conv2d_nchw_1[9] + bias[(((floordiv(blockIdx.x, 7)*16) + threadIdx.x) + 8)]), 0f32)
+        compute[((((floordiv(blockIdx.x, 7)*784) + (threadIdx.x*49)) + (floormod(blockIdx.x, 7)*7)) + 395)] = max((conv2d_nchw_1[10] + bias[(((floordiv(blockIdx.x, 7)*16) + threadIdx.x) + 8)]), 0f32)
+        compute[((((floordiv(blockIdx.x, 7)*784) + (threadIdx.x*49)) + (floormod(blockIdx.x, 7)*7)) + 396)] = max((conv2d_nchw_1[11] + bias[(((floordiv(blockIdx.x, 7)*16) + threadIdx.x) + 8)]), 0f32)
+        compute[((((floordiv(blockIdx.x, 7)*784) + (threadIdx.x*49)) + (floormod(blockIdx.x, 7)*7)) + 397)] = max((conv2d_nchw_1[12] + bias[(((floordiv(blockIdx.x, 7)*16) + threadIdx.x) + 8)]), 0f32)
+        compute[((((floordiv(blockIdx.x, 7)*784) + (threadIdx.x*49)) + (floormod(blockIdx.x, 7)*7)) + 398)] = max((conv2d_nchw_1[13] + bias[(((floordiv(blockIdx.x, 7)*16) + threadIdx.x) + 8)]), 0f32)
       }
     }
 
@@ -616,7 +764,7 @@ We build the binary and check its correctness and performance.
 
  .. code-block:: none
 
-    Execution time of this operator: 0.315 ms
+    Execution time of this operator: 0.392 ms
 
 
 
@@ -661,19 +809,19 @@ They can be used for debugging and learning the behavior of the auto-scheduler.
     conv2d_nchw_nn_o_o_o_i, conv2d_nchw_nn_o_o_i = s[conv2d_nchw].split(conv2d_nchw_nn_o_o_i, factor=1)
     conv2d_nchw_nn_o_o_o_o, conv2d_nchw_nn_o_o_o_i = s[conv2d_nchw].split(conv2d_nchw_nn_o_o_o_i, factor=1)
     conv2d_nchw_ff_o_i, conv2d_nchw_ff_i = s[conv2d_nchw].split(conv2d_nchw_ff, factor=1)
-    conv2d_nchw_ff_o_o_i, conv2d_nchw_ff_o_i = s[conv2d_nchw].split(conv2d_nchw_ff_o_i, factor=4)
-    conv2d_nchw_ff_o_o_o_i, conv2d_nchw_ff_o_o_i = s[conv2d_nchw].split(conv2d_nchw_ff_o_o_i, factor=4)
-    conv2d_nchw_ff_o_o_o_o, conv2d_nchw_ff_o_o_o_i = s[conv2d_nchw].split(conv2d_nchw_ff_o_o_o_i, factor=1)
+    conv2d_nchw_ff_o_o_i, conv2d_nchw_ff_o_i = s[conv2d_nchw].split(conv2d_nchw_ff_o_i, factor=1)
+    conv2d_nchw_ff_o_o_o_i, conv2d_nchw_ff_o_o_i = s[conv2d_nchw].split(conv2d_nchw_ff_o_o_i, factor=8)
+    conv2d_nchw_ff_o_o_o_o, conv2d_nchw_ff_o_o_o_i = s[conv2d_nchw].split(conv2d_nchw_ff_o_o_o_i, factor=2)
     conv2d_nchw_yy_o_i, conv2d_nchw_yy_i = s[conv2d_nchw].split(conv2d_nchw_yy, factor=1)
     conv2d_nchw_yy_o_o_i, conv2d_nchw_yy_o_i = s[conv2d_nchw].split(conv2d_nchw_yy_o_i, factor=1)
     conv2d_nchw_yy_o_o_o_i, conv2d_nchw_yy_o_o_i = s[conv2d_nchw].split(conv2d_nchw_yy_o_o_i, factor=1)
-    conv2d_nchw_yy_o_o_o_o, conv2d_nchw_yy_o_o_o_i = s[conv2d_nchw].split(conv2d_nchw_yy_o_o_o_i, factor=7)
+    conv2d_nchw_yy_o_o_o_o, conv2d_nchw_yy_o_o_o_i = s[conv2d_nchw].split(conv2d_nchw_yy_o_o_o_i, factor=1)
     conv2d_nchw_xx_o_i, conv2d_nchw_xx_i = s[conv2d_nchw].split(conv2d_nchw_xx, factor=1)
     conv2d_nchw_xx_o_o_i, conv2d_nchw_xx_o_i = s[conv2d_nchw].split(conv2d_nchw_xx_o_i, factor=1)
-    conv2d_nchw_xx_o_o_o_i, conv2d_nchw_xx_o_o_i = s[conv2d_nchw].split(conv2d_nchw_xx_o_o_i, factor=7)
-    conv2d_nchw_xx_o_o_o_o, conv2d_nchw_xx_o_o_o_i = s[conv2d_nchw].split(conv2d_nchw_xx_o_o_o_i, factor=1)
-    conv2d_nchw_rc_o_i, conv2d_nchw_rc_i = s[conv2d_nchw].split(conv2d_nchw_rc, factor=8)
-    conv2d_nchw_rc_o_o, conv2d_nchw_rc_o_i = s[conv2d_nchw].split(conv2d_nchw_rc_o_i, factor=1)
+    conv2d_nchw_xx_o_o_o_i, conv2d_nchw_xx_o_o_i = s[conv2d_nchw].split(conv2d_nchw_xx_o_o_i, factor=1)
+    conv2d_nchw_xx_o_o_o_o, conv2d_nchw_xx_o_o_o_i = s[conv2d_nchw].split(conv2d_nchw_xx_o_o_o_i, factor=7)
+    conv2d_nchw_rc_o_i, conv2d_nchw_rc_i = s[conv2d_nchw].split(conv2d_nchw_rc, factor=1)
+    conv2d_nchw_rc_o_o, conv2d_nchw_rc_o_i = s[conv2d_nchw].split(conv2d_nchw_rc_o_i, factor=8)
     conv2d_nchw_ry_o_i, conv2d_nchw_ry_i = s[conv2d_nchw].split(conv2d_nchw_ry, factor=1)
     conv2d_nchw_ry_o_o, conv2d_nchw_ry_o_i = s[conv2d_nchw].split(conv2d_nchw_ry_o_i, factor=1)
     conv2d_nchw_rx_o_i, conv2d_nchw_rx_i = s[conv2d_nchw].split(conv2d_nchw_rx, factor=1)
@@ -682,15 +830,15 @@ They can be used for debugging and learning the behavior of the auto-scheduler.
     compute_i0_o_i, compute_i0_i = s[compute].split(compute_i0, factor=1)
     compute_i0_o_o_i, compute_i0_o_i = s[compute].split(compute_i0_o_i, factor=1)
     compute_i0_o_o_o, compute_i0_o_o_i = s[compute].split(compute_i0_o_o_i, factor=1)
-    compute_i1_o_i, compute_i1_i = s[compute].split(compute_i1, factor=4)
-    compute_i1_o_o_i, compute_i1_o_i = s[compute].split(compute_i1_o_i, factor=4)
-    compute_i1_o_o_o, compute_i1_o_o_i = s[compute].split(compute_i1_o_o_i, factor=1)
+    compute_i1_o_i, compute_i1_i = s[compute].split(compute_i1, factor=1)
+    compute_i1_o_o_i, compute_i1_o_i = s[compute].split(compute_i1_o_i, factor=8)
+    compute_i1_o_o_o, compute_i1_o_o_i = s[compute].split(compute_i1_o_o_i, factor=2)
     compute_i2_o_i, compute_i2_i = s[compute].split(compute_i2, factor=1)
     compute_i2_o_o_i, compute_i2_o_i = s[compute].split(compute_i2_o_i, factor=1)
-    compute_i2_o_o_o, compute_i2_o_o_i = s[compute].split(compute_i2_o_o_i, factor=7)
+    compute_i2_o_o_o, compute_i2_o_o_i = s[compute].split(compute_i2_o_o_i, factor=1)
     compute_i3_o_i, compute_i3_i = s[compute].split(compute_i3, factor=1)
-    compute_i3_o_o_i, compute_i3_o_i = s[compute].split(compute_i3_o_i, factor=7)
-    compute_i3_o_o_o, compute_i3_o_o_i = s[compute].split(compute_i3_o_o_i, factor=1)
+    compute_i3_o_o_i, compute_i3_o_i = s[compute].split(compute_i3_o_i, factor=1)
+    compute_i3_o_o_o, compute_i3_o_o_i = s[compute].split(compute_i3_o_o_i, factor=7)
     s[compute].reorder(compute_i0_o_o_o, compute_i1_o_o_o, compute_i2_o_o_o, compute_i3_o_o_o, compute_i0_o_o_i, compute_i1_o_o_i, compute_i2_o_o_i, compute_i3_o_o_i, compute_i0_o_i, compute_i1_o_i, compute_i2_o_i, compute_i3_o_i, compute_i0_i, compute_i1_i, compute_i2_i, compute_i3_i)
     s[conv2d_nchw].compute_at(s[compute], compute_i3_o_i)
     kernel_shared = s.cache_read(kernel, "shared", [conv2d_nchw])
@@ -709,12 +857,12 @@ They can be used for debugging and learning the behavior of the auto-scheduler.
     kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused = s[kernel_shared].fuse(kernel_shared_ax0, kernel_shared_ax1, kernel_shared_ax2, kernel_shared_ax3)
     kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o, kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_i = s[kernel_shared].split(kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused, factor=1)
     s[kernel_shared].vectorize(kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_i)
-    kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_o, kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_i = s[kernel_shared].split(kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o, factor=28)
+    kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_o, kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_i = s[kernel_shared].split(kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o, factor=8)
     s[kernel_shared].bind(kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_i, te.thread_axis("threadIdx.x"))
     pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused = s[pad_temp_shared].fuse(pad_temp_shared_ax0, pad_temp_shared_ax1, pad_temp_shared_ax2, pad_temp_shared_ax3)
     pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o, pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_i = s[pad_temp_shared].split(pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused, factor=1)
     s[pad_temp_shared].vectorize(pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_i)
-    pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_o, pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_i = s[pad_temp_shared].split(pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o, factor=28)
+    pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_o, pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_i = s[pad_temp_shared].split(pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o, factor=8)
     s[pad_temp_shared].bind(pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_i, te.thread_axis("threadIdx.x"))
     s[conv2d_nchw].pragma(conv2d_nchw_nn_o_o_o_o, "auto_unroll_max_step", 512)
     s[conv2d_nchw].pragma(conv2d_nchw_nn_o_o_o_o, "unroll_explicit", True)
@@ -734,313 +882,437 @@ They can be used for debugging and learning the behavior of the auto-scheduler.
       #define int64_t long long
       #define uint64_t unsigned long long
     #endif
-    extern "C" __global__ void __launch_bounds__(28) default_function_kernel0(float* __restrict__ data, float* __restrict__ kernel, float* __restrict__ compute, float* __restrict__ bias) {
-      float conv2d_nchw[28];
-      __shared__ float pad_temp_shared[504];
+    extern "C" __global__ void __launch_bounds__(8) default_function_kernel0(float* __restrict__ data, float* __restrict__ kernel, float* __restrict__ compute, float* __restrict__ bias) {
+      float conv2d_nchw[14];
+      __shared__ float pad_temp_shared[72];
       __shared__ float kernel_shared[384];
       conv2d_nchw[0] = 0.000000e+00f;
-      conv2d_nchw[4] = 0.000000e+00f;
-      conv2d_nchw[8] = 0.000000e+00f;
-      conv2d_nchw[12] = 0.000000e+00f;
-      conv2d_nchw[16] = 0.000000e+00f;
-      conv2d_nchw[20] = 0.000000e+00f;
-      conv2d_nchw[24] = 0.000000e+00f;
       conv2d_nchw[1] = 0.000000e+00f;
-      conv2d_nchw[5] = 0.000000e+00f;
-      conv2d_nchw[9] = 0.000000e+00f;
-      conv2d_nchw[13] = 0.000000e+00f;
-      conv2d_nchw[17] = 0.000000e+00f;
-      conv2d_nchw[21] = 0.000000e+00f;
-      conv2d_nchw[25] = 0.000000e+00f;
       conv2d_nchw[2] = 0.000000e+00f;
-      conv2d_nchw[6] = 0.000000e+00f;
-      conv2d_nchw[10] = 0.000000e+00f;
-      conv2d_nchw[14] = 0.000000e+00f;
-      conv2d_nchw[18] = 0.000000e+00f;
-      conv2d_nchw[22] = 0.000000e+00f;
-      conv2d_nchw[26] = 0.000000e+00f;
       conv2d_nchw[3] = 0.000000e+00f;
+      conv2d_nchw[4] = 0.000000e+00f;
+      conv2d_nchw[5] = 0.000000e+00f;
+      conv2d_nchw[6] = 0.000000e+00f;
       conv2d_nchw[7] = 0.000000e+00f;
+      conv2d_nchw[8] = 0.000000e+00f;
+      conv2d_nchw[9] = 0.000000e+00f;
+      conv2d_nchw[10] = 0.000000e+00f;
       conv2d_nchw[11] = 0.000000e+00f;
-      conv2d_nchw[15] = 0.000000e+00f;
-      conv2d_nchw[19] = 0.000000e+00f;
-      conv2d_nchw[23] = 0.000000e+00f;
-      conv2d_nchw[27] = 0.000000e+00f;
+      conv2d_nchw[12] = 0.000000e+00f;
+      conv2d_nchw[13] = 0.000000e+00f;
       for (int rc_outer_outer = 0; rc_outer_outer < 64; ++rc_outer_outer) {
         for (int ry_outer_outer = 0; ry_outer_outer < 3; ++ry_outer_outer) {
           __syncthreads();
-          pad_temp_shared[((int)threadIdx.x)] = ((((1 <= ((((int)threadIdx.x) / 9) + ry_outer_outer)) && (1 <= (((int)threadIdx.x) % 9))) && ((((int)threadIdx.x) % 9) < 8)) ? data[(((((rc_outer_outer * 392) + ((((int)threadIdx.x) / 9) * 7)) + (ry_outer_outer * 7)) + (((int)threadIdx.x) % 9)) - 8)] : 0.000000e+00f);
-          pad_temp_shared[(((int)threadIdx.x) + 28)] = (((((((((int)threadIdx.x) + 28) / 9) + ry_outer_outer) < 8) && (1 <= ((((int)threadIdx.x) + 1) % 9))) && (((((int)threadIdx.x) + 1) % 9) < 8)) ? data[(((((rc_outer_outer * 392) + (((((int)threadIdx.x) + 28) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 1) % 9)) - 8)] : 0.000000e+00f);
-          pad_temp_shared[(((int)threadIdx.x) + 56)] = (((((1 <= ((((((int)threadIdx.x) + 56) % 63) / 9) + ry_outer_outer)) && (((((((int)threadIdx.x) + 56) % 63) / 9) + ry_outer_outer) < 8)) && (1 <= ((((int)threadIdx.x) + 2) % 9))) && (((((int)threadIdx.x) + 2) % 9) < 8)) ? data[(((((rc_outer_outer * 392) + (((((int)threadIdx.x) + 56) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 2) % 9)) - 8)] : 0.000000e+00f);
-          pad_temp_shared[(((int)threadIdx.x) + 84)] = (((1 <= ((((int)threadIdx.x) + 3) % 9)) && (((((int)threadIdx.x) + 3) % 9) < 8)) ? data[(((((rc_outer_outer * 392) + (((((int)threadIdx.x) + 84) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 3) % 9)) - 8)] : 0.000000e+00f);
-          pad_temp_shared[(((int)threadIdx.x) + 112)] = (((((1 <= ((((((int)threadIdx.x) + 49) % 63) / 9) + ry_outer_outer)) && (((((((int)threadIdx.x) + 49) % 63) / 9) + ry_outer_outer) < 8)) && (1 <= ((((int)threadIdx.x) + 4) % 9))) && (((((int)threadIdx.x) + 4) % 9) < 8)) ? data[(((((rc_outer_outer * 392) + (((((int)threadIdx.x) + 112) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 4) % 9)) - 8)] : 0.000000e+00f);
-          pad_temp_shared[(((int)threadIdx.x) + 140)] = (((1 <= ((((int)threadIdx.x) + 5) % 9)) && (((((int)threadIdx.x) + 5) % 9) < 8)) ? data[(((((rc_outer_outer * 392) + (((((int)threadIdx.x) + 140) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 5) % 9)) - 8)] : 0.000000e+00f);
-          pad_temp_shared[(((int)threadIdx.x) + 168)] = (((((1 <= ((((((int)threadIdx.x) + 42) % 63) / 9) + ry_outer_outer)) && (((((((int)threadIdx.x) + 42) % 63) / 9) + ry_outer_outer) < 8)) && (1 <= ((((int)threadIdx.x) + 6) % 9))) && (((((int)threadIdx.x) + 6) % 9) < 8)) ? data[(((((rc_outer_outer * 392) + (((((int)threadIdx.x) + 168) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 6) % 9)) - 8)] : 0.000000e+00f);
-          pad_temp_shared[(((int)threadIdx.x) + 196)] = ((((1 <= ((((((int)threadIdx.x) + 7) % 63) / 9) + ry_outer_outer)) && (1 <= ((((int)threadIdx.x) + 7) % 9))) && (((((int)threadIdx.x) + 7) % 9) < 8)) ? data[(((((rc_outer_outer * 392) + (((((int)threadIdx.x) + 196) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 7) % 9)) - 8)] : 0.000000e+00f);
-          pad_temp_shared[(((int)threadIdx.x) + 224)] = ((((((((((int)threadIdx.x) + 35) % 63) / 9) + ry_outer_outer) < 8) && (1 <= ((((int)threadIdx.x) + 8) % 9))) && (((((int)threadIdx.x) + 8) % 9) < 8)) ? data[(((((rc_outer_outer * 392) + (((((int)threadIdx.x) + 224) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 8) % 9)) - 8)] : 0.000000e+00f);
-          pad_temp_shared[(((int)threadIdx.x) + 252)] = ((((1 <= ((((int)threadIdx.x) / 9) + ry_outer_outer)) && (1 <= (((int)threadIdx.x) % 9))) && ((((int)threadIdx.x) % 9) < 8)) ? data[(((((rc_outer_outer * 392) + ((((int)threadIdx.x) / 9) * 7)) + (ry_outer_outer * 7)) + (((int)threadIdx.x) % 9)) + 188)] : 0.000000e+00f);
-          pad_temp_shared[(((int)threadIdx.x) + 280)] = ((((((((((int)threadIdx.x) + 28) % 63) / 9) + ry_outer_outer) < 8) && (1 <= ((((int)threadIdx.x) + 1) % 9))) && (((((int)threadIdx.x) + 1) % 9) < 8)) ? data[(((((rc_outer_outer * 392) + (((((int)threadIdx.x) + 280) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 1) % 9)) - 8)] : 0.000000e+00f);
-          pad_temp_shared[(((int)threadIdx.x) + 308)] = (((((1 <= ((((((int)threadIdx.x) + 56) % 63) / 9) + ry_outer_outer)) && (((((((int)threadIdx.x) + 56) % 63) / 9) + ry_outer_outer) < 8)) && (1 <= ((((int)threadIdx.x) + 2) % 9))) && (((((int)threadIdx.x) + 2) % 9) < 8)) ? data[(((((rc_outer_outer * 392) + (((((int)threadIdx.x) + 308) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 2) % 9)) - 8)] : 0.000000e+00f);
-          pad_temp_shared[(((int)threadIdx.x) + 336)] = (((1 <= ((((int)threadIdx.x) + 3) % 9)) && (((((int)threadIdx.x) + 3) % 9) < 8)) ? data[(((((rc_outer_outer * 392) + (((((int)threadIdx.x) + 336) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 3) % 9)) - 8)] : 0.000000e+00f);
-          pad_temp_shared[(((int)threadIdx.x) + 364)] = (((((1 <= ((((((int)threadIdx.x) + 49) % 63) / 9) + ry_outer_outer)) && (((((((int)threadIdx.x) + 49) % 63) / 9) + ry_outer_outer) < 8)) && (1 <= ((((int)threadIdx.x) + 4) % 9))) && (((((int)threadIdx.x) + 4) % 9) < 8)) ? data[(((((rc_outer_outer * 392) + (((((int)threadIdx.x) + 364) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 4) % 9)) - 8)] : 0.000000e+00f);
-          pad_temp_shared[(((int)threadIdx.x) + 392)] = (((1 <= ((((int)threadIdx.x) + 5) % 9)) && (((((int)threadIdx.x) + 5) % 9) < 8)) ? data[(((((rc_outer_outer * 392) + (((((int)threadIdx.x) + 392) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 5) % 9)) - 8)] : 0.000000e+00f);
-          pad_temp_shared[(((int)threadIdx.x) + 420)] = (((((1 <= ((((((int)threadIdx.x) + 42) % 63) / 9) + ry_outer_outer)) && (((((((int)threadIdx.x) + 42) % 63) / 9) + ry_outer_outer) < 8)) && (1 <= ((((int)threadIdx.x) + 6) % 9))) && (((((int)threadIdx.x) + 6) % 9) < 8)) ? data[(((((rc_outer_outer * 392) + (((((int)threadIdx.x) + 420) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 6) % 9)) - 8)] : 0.000000e+00f);
-          pad_temp_shared[(((int)threadIdx.x) + 448)] = ((((1 <= ((((((int)threadIdx.x) + 7) % 63) / 9) + ry_outer_outer)) && (1 <= ((((int)threadIdx.x) + 7) % 9))) && (((((int)threadIdx.x) + 7) % 9) < 8)) ? data[(((((rc_outer_outer * 392) + (((((int)threadIdx.x) + 448) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 7) % 9)) - 8)] : 0.000000e+00f);
-          pad_temp_shared[(((int)threadIdx.x) + 476)] = ((((((((((int)threadIdx.x) + 35) % 63) / 9) + ry_outer_outer) < 8) && (1 <= ((((int)threadIdx.x) + 8) % 9))) && (((((int)threadIdx.x) + 8) % 9) < 8)) ? data[(((((rc_outer_outer * 392) + (((((int)threadIdx.x) + 476) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 8) % 9)) - 8)] : 0.000000e+00f);
-          kernel_shared[((int)threadIdx.x)] = kernel[((((((((int)blockIdx.x) * 73728) + ((((int)threadIdx.x) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3))];
-          kernel_shared[(((int)threadIdx.x) + 28)] = kernel[((((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 28) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 4) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
-          kernel_shared[(((int)threadIdx.x) + 56)] = kernel[((((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 56) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 8) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
-          kernel_shared[(((int)threadIdx.x) + 84)] = kernel[((((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 84) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) / 3) + 4) & 7) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3))];
-          kernel_shared[(((int)threadIdx.x) + 112)] = kernel[((((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 112) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 16) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
-          kernel_shared[(((int)threadIdx.x) + 140)] = kernel[((((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 140) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 20) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
-          kernel_shared[(((int)threadIdx.x) + 168)] = kernel[(((((((((int)blockIdx.x) * 73728) + ((((int)threadIdx.x) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 32256)];
-          kernel_shared[(((int)threadIdx.x) + 196)] = kernel[((((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 196) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 4) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
-          kernel_shared[(((int)threadIdx.x) + 224)] = kernel[((((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 224) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 8) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
-          kernel_shared[(((int)threadIdx.x) + 252)] = kernel[((((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 252) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) / 3) + 4) & 7) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3))];
-          kernel_shared[(((int)threadIdx.x) + 280)] = kernel[((((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 280) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 16) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
-          kernel_shared[(((int)threadIdx.x) + 308)] = kernel[((((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 308) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 20) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
-          kernel_shared[(((int)threadIdx.x) + 336)] = kernel[(((((((((int)blockIdx.x) * 73728) + ((((int)threadIdx.x) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 64512)];
-          if (((int)threadIdx.x) < 20) {
-            kernel_shared[(((int)threadIdx.x) + 364)] = kernel[((((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 364) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 4) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
-          }
+          pad_temp_shared[((int)threadIdx.x)] = ((((1 <= (ry_outer_outer + (((int)blockIdx.x) % 7))) && ((ry_outer_outer + (((int)blockIdx.x) % 7)) < 8)) && (1 <= ((int)threadIdx.x))) ? data[(((((rc_outer_outer * 392) + (ry_outer_outer * 7)) + ((((int)blockIdx.x) % 7) * 7)) + ((int)threadIdx.x)) - 8)] : 0.000000e+00f);
+          pad_temp_shared[(((int)threadIdx.x) + 8)] = (((((1 <= (ry_outer_outer + (((int)blockIdx.x) % 7))) && ((ry_outer_outer + (((int)blockIdx.x) % 7)) < 8)) && (1 <= ((((int)threadIdx.x) + 8) % 9))) && (((((int)threadIdx.x) + 8) % 9) < 8)) ? data[((((((rc_outer_outer * 392) + (((((int)threadIdx.x) + 8) / 9) * 49)) + (ry_outer_outer * 7)) + ((((int)blockIdx.x) % 7) * 7)) + ((((int)threadIdx.x) + 8) % 9)) - 8)] : 0.000000e+00f);
+          pad_temp_shared[(((int)threadIdx.x) + 16)] = (((((1 <= (ry_outer_outer + (((int)blockIdx.x) % 7))) && ((ry_outer_outer + (((int)blockIdx.x) % 7)) < 8)) && (1 <= ((((int)threadIdx.x) + 7) % 9))) && (((((int)threadIdx.x) + 7) % 9) < 8)) ? data[((((((rc_outer_outer * 392) + (((((int)threadIdx.x) + 16) / 9) * 49)) + (ry_outer_outer * 7)) + ((((int)blockIdx.x) % 7) * 7)) + ((((int)threadIdx.x) + 7) % 9)) - 8)] : 0.000000e+00f);
+          pad_temp_shared[(((int)threadIdx.x) + 24)] = (((((1 <= (ry_outer_outer + (((int)blockIdx.x) % 7))) && ((ry_outer_outer + (((int)blockIdx.x) % 7)) < 8)) && (1 <= ((((int)threadIdx.x) + 6) % 9))) && (((((int)threadIdx.x) + 6) % 9) < 8)) ? data[((((((rc_outer_outer * 392) + (((((int)threadIdx.x) + 24) / 9) * 49)) + (ry_outer_outer * 7)) + ((((int)blockIdx.x) % 7) * 7)) + ((((int)threadIdx.x) + 6) % 9)) - 8)] : 0.000000e+00f);
+          pad_temp_shared[(((int)threadIdx.x) + 32)] = (((((1 <= (ry_outer_outer + (((int)blockIdx.x) % 7))) && ((ry_outer_outer + (((int)blockIdx.x) % 7)) < 8)) && (1 <= ((((int)threadIdx.x) + 5) % 9))) && (((((int)threadIdx.x) + 5) % 9) < 8)) ? data[((((((rc_outer_outer * 392) + (((((int)threadIdx.x) + 32) / 9) * 49)) + (ry_outer_outer * 7)) + ((((int)blockIdx.x) % 7) * 7)) + ((((int)threadIdx.x) + 5) % 9)) - 8)] : 0.000000e+00f);
+          pad_temp_shared[(((int)threadIdx.x) + 40)] = (((((1 <= (ry_outer_outer + (((int)blockIdx.x) % 7))) && ((ry_outer_outer + (((int)blockIdx.x) % 7)) < 8)) && (1 <= ((((int)threadIdx.x) + 4) % 9))) && (((((int)threadIdx.x) + 4) % 9) < 8)) ? data[((((((rc_outer_outer * 392) + (((((int)threadIdx.x) + 40) / 9) * 49)) + (ry_outer_outer * 7)) + ((((int)blockIdx.x) % 7) * 7)) + ((((int)threadIdx.x) + 4) % 9)) - 8)] : 0.000000e+00f);
+          pad_temp_shared[(((int)threadIdx.x) + 48)] = (((((1 <= (ry_outer_outer + (((int)blockIdx.x) % 7))) && ((ry_outer_outer + (((int)blockIdx.x) % 7)) < 8)) && (1 <= ((((int)threadIdx.x) + 3) % 9))) && (((((int)threadIdx.x) + 3) % 9) < 8)) ? data[((((((rc_outer_outer * 392) + (((((int)threadIdx.x) + 48) / 9) * 49)) + (ry_outer_outer * 7)) + ((((int)blockIdx.x) % 7) * 7)) + ((((int)threadIdx.x) + 3) % 9)) - 8)] : 0.000000e+00f);
+          pad_temp_shared[(((int)threadIdx.x) + 56)] = (((((1 <= (ry_outer_outer + (((int)blockIdx.x) % 7))) && ((ry_outer_outer + (((int)blockIdx.x) % 7)) < 8)) && (1 <= ((((int)threadIdx.x) + 2) % 9))) && (((((int)threadIdx.x) + 2) % 9) < 8)) ? data[((((((rc_outer_outer * 392) + (((((int)threadIdx.x) + 56) / 9) * 49)) + (ry_outer_outer * 7)) + ((((int)blockIdx.x) % 7) * 7)) + ((((int)threadIdx.x) + 2) % 9)) - 8)] : 0.000000e+00f);
+          pad_temp_shared[(((int)threadIdx.x) + 64)] = ((((1 <= (ry_outer_outer + (((int)blockIdx.x) % 7))) && ((ry_outer_outer + (((int)blockIdx.x) % 7)) < 8)) && (((int)threadIdx.x) < 7)) ? data[((((((rc_outer_outer * 392) + (((((int)threadIdx.x) + 64) / 9) * 49)) + (ry_outer_outer * 7)) + ((((int)blockIdx.x) % 7) * 7)) + (((int)threadIdx.x) + 1)) - 8)] : 0.000000e+00f);
+          kernel_shared[((int)threadIdx.x)] = kernel[((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + ((((int)threadIdx.x) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3))];
+          kernel_shared[(((int)threadIdx.x) + 8)] = kernel[((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 8) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
+          kernel_shared[(((int)threadIdx.x) + 16)] = kernel[((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 16) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
+          kernel_shared[(((int)threadIdx.x) + 24)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + ((((int)threadIdx.x) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 4608)];
+          kernel_shared[(((int)threadIdx.x) + 32)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 8) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3)) + 4608)];
+          kernel_shared[(((int)threadIdx.x) + 40)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 16) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3)) + 4608)];
+          kernel_shared[(((int)threadIdx.x) + 48)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + ((((int)threadIdx.x) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 9216)];
+          kernel_shared[(((int)threadIdx.x) + 56)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 8) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3)) + 9216)];
+          kernel_shared[(((int)threadIdx.x) + 64)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 16) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3)) + 9216)];
+          kernel_shared[(((int)threadIdx.x) + 72)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + ((((int)threadIdx.x) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 13824)];
+          kernel_shared[(((int)threadIdx.x) + 80)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 8) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3)) + 13824)];
+          kernel_shared[(((int)threadIdx.x) + 88)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 16) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3)) + 13824)];
+          kernel_shared[(((int)threadIdx.x) + 96)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + ((((int)threadIdx.x) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 18432)];
+          kernel_shared[(((int)threadIdx.x) + 104)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 8) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3)) + 18432)];
+          kernel_shared[(((int)threadIdx.x) + 112)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 16) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3)) + 18432)];
+          kernel_shared[(((int)threadIdx.x) + 120)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + ((((int)threadIdx.x) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 23040)];
+          kernel_shared[(((int)threadIdx.x) + 128)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 8) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3)) + 23040)];
+          kernel_shared[(((int)threadIdx.x) + 136)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 16) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3)) + 23040)];
+          kernel_shared[(((int)threadIdx.x) + 144)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + ((((int)threadIdx.x) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 27648)];
+          kernel_shared[(((int)threadIdx.x) + 152)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 8) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3)) + 27648)];
+          kernel_shared[(((int)threadIdx.x) + 160)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 16) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3)) + 27648)];
+          kernel_shared[(((int)threadIdx.x) + 168)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + ((((int)threadIdx.x) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 32256)];
+          kernel_shared[(((int)threadIdx.x) + 176)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 8) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3)) + 32256)];
+          kernel_shared[(((int)threadIdx.x) + 184)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 16) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3)) + 32256)];
+          kernel_shared[(((int)threadIdx.x) + 192)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + ((((int)threadIdx.x) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 36864)];
+          kernel_shared[(((int)threadIdx.x) + 200)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 8) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3)) + 36864)];
+          kernel_shared[(((int)threadIdx.x) + 208)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 16) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3)) + 36864)];
+          kernel_shared[(((int)threadIdx.x) + 216)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + ((((int)threadIdx.x) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 41472)];
+          kernel_shared[(((int)threadIdx.x) + 224)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 8) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3)) + 41472)];
+          kernel_shared[(((int)threadIdx.x) + 232)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 16) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3)) + 41472)];
+          kernel_shared[(((int)threadIdx.x) + 240)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + ((((int)threadIdx.x) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 46080)];
+          kernel_shared[(((int)threadIdx.x) + 248)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 8) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3)) + 46080)];
+          kernel_shared[(((int)threadIdx.x) + 256)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 16) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3)) + 46080)];
+          kernel_shared[(((int)threadIdx.x) + 264)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + ((((int)threadIdx.x) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 50688)];
+          kernel_shared[(((int)threadIdx.x) + 272)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 8) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3)) + 50688)];
+          kernel_shared[(((int)threadIdx.x) + 280)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 16) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3)) + 50688)];
+          kernel_shared[(((int)threadIdx.x) + 288)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + ((((int)threadIdx.x) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 55296)];
+          kernel_shared[(((int)threadIdx.x) + 296)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 8) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3)) + 55296)];
+          kernel_shared[(((int)threadIdx.x) + 304)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 16) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3)) + 55296)];
+          kernel_shared[(((int)threadIdx.x) + 312)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + ((((int)threadIdx.x) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 59904)];
+          kernel_shared[(((int)threadIdx.x) + 320)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 8) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3)) + 59904)];
+          kernel_shared[(((int)threadIdx.x) + 328)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 16) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3)) + 59904)];
+          kernel_shared[(((int)threadIdx.x) + 336)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + ((((int)threadIdx.x) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 64512)];
+          kernel_shared[(((int)threadIdx.x) + 344)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 8) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3)) + 64512)];
+          kernel_shared[(((int)threadIdx.x) + 352)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 16) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3)) + 64512)];
+          kernel_shared[(((int)threadIdx.x) + 360)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + ((((int)threadIdx.x) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 69120)];
+          kernel_shared[(((int)threadIdx.x) + 368)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 8) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3)) + 69120)];
+          kernel_shared[(((int)threadIdx.x) + 376)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 16) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3)) + 69120)];
           __syncthreads();
-          for (int rx_outer_inner = 0; rx_outer_inner < 3; ++rx_outer_inner) {
-            conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(rx_outer_inner + (((int)threadIdx.x) % 7))] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + rx_outer_inner)]));
-            conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 9)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + rx_outer_inner)]));
-            conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 18)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + rx_outer_inner)]));
-            conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 27)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + rx_outer_inner)]));
-            conv2d_nchw[16] = (conv2d_nchw[16] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 36)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + rx_outer_inner)]));
-            conv2d_nchw[20] = (conv2d_nchw[20] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 45)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + rx_outer_inner)]));
-            conv2d_nchw[24] = (conv2d_nchw[24] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 54)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + rx_outer_inner)]));
-            conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 63)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 3)]));
-            conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 72)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 3)]));
-            conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 81)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 3)]));
-            conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 90)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 3)]));
-            conv2d_nchw[16] = (conv2d_nchw[16] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 99)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 3)]));
-            conv2d_nchw[20] = (conv2d_nchw[20] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 108)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 3)]));
-            conv2d_nchw[24] = (conv2d_nchw[24] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 117)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 3)]));
-            conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 126)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 6)]));
-            conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 135)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 6)]));
-            conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 144)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 6)]));
-            conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 153)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 6)]));
-            conv2d_nchw[16] = (conv2d_nchw[16] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 162)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 6)]));
-            conv2d_nchw[20] = (conv2d_nchw[20] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 171)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 6)]));
-            conv2d_nchw[24] = (conv2d_nchw[24] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 180)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 6)]));
-            conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 189)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 9)]));
-            conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 198)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 9)]));
-            conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 207)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 9)]));
-            conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 216)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 9)]));
-            conv2d_nchw[16] = (conv2d_nchw[16] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 225)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 9)]));
-            conv2d_nchw[20] = (conv2d_nchw[20] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 234)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 9)]));
-            conv2d_nchw[24] = (conv2d_nchw[24] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 243)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 9)]));
-            conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 252)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 12)]));
-            conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 261)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 12)]));
-            conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 270)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 12)]));
-            conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 279)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 12)]));
-            conv2d_nchw[16] = (conv2d_nchw[16] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 288)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 12)]));
-            conv2d_nchw[20] = (conv2d_nchw[20] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 297)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 12)]));
-            conv2d_nchw[24] = (conv2d_nchw[24] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 306)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 12)]));
-            conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 315)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 15)]));
-            conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 324)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 15)]));
-            conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 333)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 15)]));
-            conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 342)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 15)]));
-            conv2d_nchw[16] = (conv2d_nchw[16] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 351)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 15)]));
-            conv2d_nchw[20] = (conv2d_nchw[20] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 360)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 15)]));
-            conv2d_nchw[24] = (conv2d_nchw[24] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 369)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 15)]));
-            conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 378)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 18)]));
-            conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 387)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 18)]));
-            conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 396)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 18)]));
-            conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 405)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 18)]));
-            conv2d_nchw[16] = (conv2d_nchw[16] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 414)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 18)]));
-            conv2d_nchw[20] = (conv2d_nchw[20] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 423)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 18)]));
-            conv2d_nchw[24] = (conv2d_nchw[24] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 432)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 18)]));
-            conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 441)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 21)]));
-            conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 450)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 21)]));
-            conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 459)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 21)]));
-            conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 468)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 21)]));
-            conv2d_nchw[16] = (conv2d_nchw[16] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 477)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 21)]));
-            conv2d_nchw[20] = (conv2d_nchw[20] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 486)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 21)]));
-            conv2d_nchw[24] = (conv2d_nchw[24] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 495)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 21)]));
-            conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(rx_outer_inner + (((int)threadIdx.x) % 7))] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 24)]));
-            conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 9)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 24)]));
-            conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 18)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 24)]));
-            conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 27)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 24)]));
-            conv2d_nchw[17] = (conv2d_nchw[17] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 36)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 24)]));
-            conv2d_nchw[21] = (conv2d_nchw[21] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 45)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 24)]));
-            conv2d_nchw[25] = (conv2d_nchw[25] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 54)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 24)]));
-            conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 63)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 27)]));
-            conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 72)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 27)]));
-            conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 81)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 27)]));
-            conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 90)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 27)]));
-            conv2d_nchw[17] = (conv2d_nchw[17] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 99)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 27)]));
-            conv2d_nchw[21] = (conv2d_nchw[21] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 108)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 27)]));
-            conv2d_nchw[25] = (conv2d_nchw[25] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 117)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 27)]));
-            conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 126)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 30)]));
-            conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 135)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 30)]));
-            conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 144)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 30)]));
-            conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 153)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 30)]));
-            conv2d_nchw[17] = (conv2d_nchw[17] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 162)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 30)]));
-            conv2d_nchw[21] = (conv2d_nchw[21] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 171)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 30)]));
-            conv2d_nchw[25] = (conv2d_nchw[25] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 180)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 30)]));
-            conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 189)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 33)]));
-            conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 198)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 33)]));
-            conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 207)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 33)]));
-            conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 216)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 33)]));
-            conv2d_nchw[17] = (conv2d_nchw[17] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 225)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 33)]));
-            conv2d_nchw[21] = (conv2d_nchw[21] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 234)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 33)]));
-            conv2d_nchw[25] = (conv2d_nchw[25] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 243)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 33)]));
-            conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 252)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 36)]));
-            conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 261)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 36)]));
-            conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 270)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 36)]));
-            conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 279)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 36)]));
-            conv2d_nchw[17] = (conv2d_nchw[17] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 288)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 36)]));
-            conv2d_nchw[21] = (conv2d_nchw[21] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 297)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 36)]));
-            conv2d_nchw[25] = (conv2d_nchw[25] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 306)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 36)]));
-            conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 315)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 39)]));
-            conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 324)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 39)]));
-            conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 333)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 39)]));
-            conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 342)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 39)]));
-            conv2d_nchw[17] = (conv2d_nchw[17] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 351)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 39)]));
-            conv2d_nchw[21] = (conv2d_nchw[21] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 360)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 39)]));
-            conv2d_nchw[25] = (conv2d_nchw[25] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 369)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 39)]));
-            conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 378)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 42)]));
-            conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 387)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 42)]));
-            conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 396)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 42)]));
-            conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 405)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 42)]));
-            conv2d_nchw[17] = (conv2d_nchw[17] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 414)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 42)]));
-            conv2d_nchw[21] = (conv2d_nchw[21] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 423)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 42)]));
-            conv2d_nchw[25] = (conv2d_nchw[25] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 432)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 42)]));
-            conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 441)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 45)]));
-            conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 450)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 45)]));
-            conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 459)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 45)]));
-            conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 468)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 45)]));
-            conv2d_nchw[17] = (conv2d_nchw[17] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 477)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 45)]));
-            conv2d_nchw[21] = (conv2d_nchw[21] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 486)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 45)]));
-            conv2d_nchw[25] = (conv2d_nchw[25] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 495)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 45)]));
-            conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(rx_outer_inner + (((int)threadIdx.x) % 7))] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 48)]));
-            conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 9)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 48)]));
-            conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 18)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 48)]));
-            conv2d_nchw[14] = (conv2d_nchw[14] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 27)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 48)]));
-            conv2d_nchw[18] = (conv2d_nchw[18] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 36)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 48)]));
-            conv2d_nchw[22] = (conv2d_nchw[22] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 45)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 48)]));
-            conv2d_nchw[26] = (conv2d_nchw[26] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 54)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 48)]));
-            conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 63)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 51)]));
-            conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 72)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 51)]));
-            conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 81)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 51)]));
-            conv2d_nchw[14] = (conv2d_nchw[14] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 90)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 51)]));
-            conv2d_nchw[18] = (conv2d_nchw[18] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 99)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 51)]));
-            conv2d_nchw[22] = (conv2d_nchw[22] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 108)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 51)]));
-            conv2d_nchw[26] = (conv2d_nchw[26] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 117)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 51)]));
-            conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 126)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 54)]));
-            conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 135)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 54)]));
-            conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 144)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 54)]));
-            conv2d_nchw[14] = (conv2d_nchw[14] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 153)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 54)]));
-            conv2d_nchw[18] = (conv2d_nchw[18] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 162)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 54)]));
-            conv2d_nchw[22] = (conv2d_nchw[22] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 171)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 54)]));
-            conv2d_nchw[26] = (conv2d_nchw[26] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 180)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 54)]));
-            conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 189)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 57)]));
-            conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 198)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 57)]));
-            conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 207)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 57)]));
-            conv2d_nchw[14] = (conv2d_nchw[14] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 216)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 57)]));
-            conv2d_nchw[18] = (conv2d_nchw[18] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 225)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 57)]));
-            conv2d_nchw[22] = (conv2d_nchw[22] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 234)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 57)]));
-            conv2d_nchw[26] = (conv2d_nchw[26] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 243)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 57)]));
-            conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 252)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 60)]));
-            conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 261)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 60)]));
-            conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 270)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 60)]));
-            conv2d_nchw[14] = (conv2d_nchw[14] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 279)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 60)]));
-            conv2d_nchw[18] = (conv2d_nchw[18] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 288)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 60)]));
-            conv2d_nchw[22] = (conv2d_nchw[22] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 297)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 60)]));
-            conv2d_nchw[26] = (conv2d_nchw[26] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 306)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 60)]));
-            conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 315)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 63)]));
-            conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 324)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 63)]));
-            conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 333)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 63)]));
-            conv2d_nchw[14] = (conv2d_nchw[14] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 342)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 63)]));
-            conv2d_nchw[18] = (conv2d_nchw[18] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 351)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 63)]));
-            conv2d_nchw[22] = (conv2d_nchw[22] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 360)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 63)]));
-            conv2d_nchw[26] = (conv2d_nchw[26] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 369)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 63)]));
-            conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 378)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 66)]));
-            conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 387)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 66)]));
-            conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 396)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 66)]));
-            conv2d_nchw[14] = (conv2d_nchw[14] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 405)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 66)]));
-            conv2d_nchw[18] = (conv2d_nchw[18] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 414)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 66)]));
-            conv2d_nchw[22] = (conv2d_nchw[22] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 423)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 66)]));
-            conv2d_nchw[26] = (conv2d_nchw[26] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 432)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 66)]));
-            conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 441)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 69)]));
-            conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 450)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 69)]));
-            conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 459)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 69)]));
-            conv2d_nchw[14] = (conv2d_nchw[14] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 468)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 69)]));
-            conv2d_nchw[18] = (conv2d_nchw[18] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 477)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 69)]));
-            conv2d_nchw[22] = (conv2d_nchw[22] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 486)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 69)]));
-            conv2d_nchw[26] = (conv2d_nchw[26] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 495)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 69)]));
-            conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(rx_outer_inner + (((int)threadIdx.x) % 7))] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 72)]));
-            conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 9)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 72)]));
-            conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 18)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 72)]));
-            conv2d_nchw[15] = (conv2d_nchw[15] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 27)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 72)]));
-            conv2d_nchw[19] = (conv2d_nchw[19] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 36)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 72)]));
-            conv2d_nchw[23] = (conv2d_nchw[23] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 45)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 72)]));
-            conv2d_nchw[27] = (conv2d_nchw[27] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 54)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 72)]));
-            conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 63)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 75)]));
-            conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 72)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 75)]));
-            conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 81)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 75)]));
-            conv2d_nchw[15] = (conv2d_nchw[15] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 90)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 75)]));
-            conv2d_nchw[19] = (conv2d_nchw[19] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 99)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 75)]));
-            conv2d_nchw[23] = (conv2d_nchw[23] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 108)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 75)]));
-            conv2d_nchw[27] = (conv2d_nchw[27] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 117)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 75)]));
-            conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 126)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 78)]));
-            conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 135)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 78)]));
-            conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 144)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 78)]));
-            conv2d_nchw[15] = (conv2d_nchw[15] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 153)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 78)]));
-            conv2d_nchw[19] = (conv2d_nchw[19] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 162)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 78)]));
-            conv2d_nchw[23] = (conv2d_nchw[23] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 171)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 78)]));
-            conv2d_nchw[27] = (conv2d_nchw[27] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 180)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 78)]));
-            conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 189)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 81)]));
-            conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 198)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 81)]));
-            conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 207)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 81)]));
-            conv2d_nchw[15] = (conv2d_nchw[15] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 216)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 81)]));
-            conv2d_nchw[19] = (conv2d_nchw[19] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 225)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 81)]));
-            conv2d_nchw[23] = (conv2d_nchw[23] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 234)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 81)]));
-            conv2d_nchw[27] = (conv2d_nchw[27] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 243)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 81)]));
-            conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 252)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 84)]));
-            conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 261)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 84)]));
-            conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 270)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 84)]));
-            conv2d_nchw[15] = (conv2d_nchw[15] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 279)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 84)]));
-            conv2d_nchw[19] = (conv2d_nchw[19] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 288)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 84)]));
-            conv2d_nchw[23] = (conv2d_nchw[23] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 297)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 84)]));
-            conv2d_nchw[27] = (conv2d_nchw[27] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 306)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 84)]));
-            conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 315)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 87)]));
-            conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 324)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 87)]));
-            conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 333)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 87)]));
-            conv2d_nchw[15] = (conv2d_nchw[15] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 342)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 87)]));
-            conv2d_nchw[19] = (conv2d_nchw[19] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 351)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 87)]));
-            conv2d_nchw[23] = (conv2d_nchw[23] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 360)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 87)]));
-            conv2d_nchw[27] = (conv2d_nchw[27] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 369)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 87)]));
-            conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 378)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 90)]));
-            conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 387)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 90)]));
-            conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 396)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 90)]));
-            conv2d_nchw[15] = (conv2d_nchw[15] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 405)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 90)]));
-            conv2d_nchw[19] = (conv2d_nchw[19] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 414)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 90)]));
-            conv2d_nchw[23] = (conv2d_nchw[23] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 423)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 90)]));
-            conv2d_nchw[27] = (conv2d_nchw[27] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 432)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 90)]));
-            conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 441)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 93)]));
-            conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 450)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 93)]));
-            conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 459)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 93)]));
-            conv2d_nchw[15] = (conv2d_nchw[15] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 468)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 93)]));
-            conv2d_nchw[19] = (conv2d_nchw[19] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 477)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 93)]));
-            conv2d_nchw[23] = (conv2d_nchw[23] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 486)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 93)]));
-            conv2d_nchw[27] = (conv2d_nchw[27] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 495)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 93)]));
-          }
+          conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[0] * kernel_shared[(((int)threadIdx.x) * 24)]));
+          conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[1] * kernel_shared[(((int)threadIdx.x) * 24)]));
+          conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[2] * kernel_shared[(((int)threadIdx.x) * 24)]));
+          conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[3] * kernel_shared[(((int)threadIdx.x) * 24)]));
+          conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[4] * kernel_shared[(((int)threadIdx.x) * 24)]));
+          conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[5] * kernel_shared[(((int)threadIdx.x) * 24)]));
+          conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[6] * kernel_shared[(((int)threadIdx.x) * 24)]));
+          conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[0] * kernel_shared[((((int)threadIdx.x) * 24) + 192)]));
+          conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[1] * kernel_shared[((((int)threadIdx.x) * 24) + 192)]));
+          conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[2] * kernel_shared[((((int)threadIdx.x) * 24) + 192)]));
+          conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[3] * kernel_shared[((((int)threadIdx.x) * 24) + 192)]));
+          conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[4] * kernel_shared[((((int)threadIdx.x) * 24) + 192)]));
+          conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[5] * kernel_shared[((((int)threadIdx.x) * 24) + 192)]));
+          conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[6] * kernel_shared[((((int)threadIdx.x) * 24) + 192)]));
+          conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[1] * kernel_shared[((((int)threadIdx.x) * 24) + 1)]));
+          conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[2] * kernel_shared[((((int)threadIdx.x) * 24) + 1)]));
+          conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[3] * kernel_shared[((((int)threadIdx.x) * 24) + 1)]));
+          conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[4] * kernel_shared[((((int)threadIdx.x) * 24) + 1)]));
+          conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[5] * kernel_shared[((((int)threadIdx.x) * 24) + 1)]));
+          conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[6] * kernel_shared[((((int)threadIdx.x) * 24) + 1)]));
+          conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[7] * kernel_shared[((((int)threadIdx.x) * 24) + 1)]));
+          conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[1] * kernel_shared[((((int)threadIdx.x) * 24) + 193)]));
+          conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[2] * kernel_shared[((((int)threadIdx.x) * 24) + 193)]));
+          conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[3] * kernel_shared[((((int)threadIdx.x) * 24) + 193)]));
+          conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[4] * kernel_shared[((((int)threadIdx.x) * 24) + 193)]));
+          conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[5] * kernel_shared[((((int)threadIdx.x) * 24) + 193)]));
+          conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[6] * kernel_shared[((((int)threadIdx.x) * 24) + 193)]));
+          conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[7] * kernel_shared[((((int)threadIdx.x) * 24) + 193)]));
+          conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[2] * kernel_shared[((((int)threadIdx.x) * 24) + 2)]));
+          conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[3] * kernel_shared[((((int)threadIdx.x) * 24) + 2)]));
+          conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[4] * kernel_shared[((((int)threadIdx.x) * 24) + 2)]));
+          conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[5] * kernel_shared[((((int)threadIdx.x) * 24) + 2)]));
+          conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[6] * kernel_shared[((((int)threadIdx.x) * 24) + 2)]));
+          conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[7] * kernel_shared[((((int)threadIdx.x) * 24) + 2)]));
+          conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[8] * kernel_shared[((((int)threadIdx.x) * 24) + 2)]));
+          conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[2] * kernel_shared[((((int)threadIdx.x) * 24) + 194)]));
+          conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[3] * kernel_shared[((((int)threadIdx.x) * 24) + 194)]));
+          conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[4] * kernel_shared[((((int)threadIdx.x) * 24) + 194)]));
+          conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[5] * kernel_shared[((((int)threadIdx.x) * 24) + 194)]));
+          conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[6] * kernel_shared[((((int)threadIdx.x) * 24) + 194)]));
+          conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[7] * kernel_shared[((((int)threadIdx.x) * 24) + 194)]));
+          conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[8] * kernel_shared[((((int)threadIdx.x) * 24) + 194)]));
+          conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[9] * kernel_shared[((((int)threadIdx.x) * 24) + 3)]));
+          conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[10] * kernel_shared[((((int)threadIdx.x) * 24) + 3)]));
+          conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[11] * kernel_shared[((((int)threadIdx.x) * 24) + 3)]));
+          conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[12] * kernel_shared[((((int)threadIdx.x) * 24) + 3)]));
+          conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[13] * kernel_shared[((((int)threadIdx.x) * 24) + 3)]));
+          conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[14] * kernel_shared[((((int)threadIdx.x) * 24) + 3)]));
+          conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[15] * kernel_shared[((((int)threadIdx.x) * 24) + 3)]));
+          conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[9] * kernel_shared[((((int)threadIdx.x) * 24) + 195)]));
+          conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[10] * kernel_shared[((((int)threadIdx.x) * 24) + 195)]));
+          conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[11] * kernel_shared[((((int)threadIdx.x) * 24) + 195)]));
+          conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[12] * kernel_shared[((((int)threadIdx.x) * 24) + 195)]));
+          conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[13] * kernel_shared[((((int)threadIdx.x) * 24) + 195)]));
+          conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[14] * kernel_shared[((((int)threadIdx.x) * 24) + 195)]));
+          conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[15] * kernel_shared[((((int)threadIdx.x) * 24) + 195)]));
+          conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[10] * kernel_shared[((((int)threadIdx.x) * 24) + 4)]));
+          conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[11] * kernel_shared[((((int)threadIdx.x) * 24) + 4)]));
+          conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[12] * kernel_shared[((((int)threadIdx.x) * 24) + 4)]));
+          conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[13] * kernel_shared[((((int)threadIdx.x) * 24) + 4)]));
+          conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[14] * kernel_shared[((((int)threadIdx.x) * 24) + 4)]));
+          conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[15] * kernel_shared[((((int)threadIdx.x) * 24) + 4)]));
+          conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[16] * kernel_shared[((((int)threadIdx.x) * 24) + 4)]));
+          conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[10] * kernel_shared[((((int)threadIdx.x) * 24) + 196)]));
+          conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[11] * kernel_shared[((((int)threadIdx.x) * 24) + 196)]));
+          conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[12] * kernel_shared[((((int)threadIdx.x) * 24) + 196)]));
+          conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[13] * kernel_shared[((((int)threadIdx.x) * 24) + 196)]));
+          conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[14] * kernel_shared[((((int)threadIdx.x) * 24) + 196)]));
+          conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[15] * kernel_shared[((((int)threadIdx.x) * 24) + 196)]));
+          conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[16] * kernel_shared[((((int)threadIdx.x) * 24) + 196)]));
+          conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[11] * kernel_shared[((((int)threadIdx.x) * 24) + 5)]));
+          conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[12] * kernel_shared[((((int)threadIdx.x) * 24) + 5)]));
+          conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[13] * kernel_shared[((((int)threadIdx.x) * 24) + 5)]));
+          conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[14] * kernel_shared[((((int)threadIdx.x) * 24) + 5)]));
+          conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[15] * kernel_shared[((((int)threadIdx.x) * 24) + 5)]));
+          conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[16] * kernel_shared[((((int)threadIdx.x) * 24) + 5)]));
+          conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[17] * kernel_shared[((((int)threadIdx.x) * 24) + 5)]));
+          conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[11] * kernel_shared[((((int)threadIdx.x) * 24) + 197)]));
+          conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[12] * kernel_shared[((((int)threadIdx.x) * 24) + 197)]));
+          conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[13] * kernel_shared[((((int)threadIdx.x) * 24) + 197)]));
+          conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[14] * kernel_shared[((((int)threadIdx.x) * 24) + 197)]));
+          conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[15] * kernel_shared[((((int)threadIdx.x) * 24) + 197)]));
+          conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[16] * kernel_shared[((((int)threadIdx.x) * 24) + 197)]));
+          conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[17] * kernel_shared[((((int)threadIdx.x) * 24) + 197)]));
+          conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[18] * kernel_shared[((((int)threadIdx.x) * 24) + 6)]));
+          conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[19] * kernel_shared[((((int)threadIdx.x) * 24) + 6)]));
+          conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[20] * kernel_shared[((((int)threadIdx.x) * 24) + 6)]));
+          conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[21] * kernel_shared[((((int)threadIdx.x) * 24) + 6)]));
+          conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[22] * kernel_shared[((((int)threadIdx.x) * 24) + 6)]));
+          conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[23] * kernel_shared[((((int)threadIdx.x) * 24) + 6)]));
+          conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[24] * kernel_shared[((((int)threadIdx.x) * 24) + 6)]));
+          conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[18] * kernel_shared[((((int)threadIdx.x) * 24) + 198)]));
+          conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[19] * kernel_shared[((((int)threadIdx.x) * 24) + 198)]));
+          conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[20] * kernel_shared[((((int)threadIdx.x) * 24) + 198)]));
+          conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[21] * kernel_shared[((((int)threadIdx.x) * 24) + 198)]));
+          conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[22] * kernel_shared[((((int)threadIdx.x) * 24) + 198)]));
+          conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[23] * kernel_shared[((((int)threadIdx.x) * 24) + 198)]));
+          conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[24] * kernel_shared[((((int)threadIdx.x) * 24) + 198)]));
+          conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[19] * kernel_shared[((((int)threadIdx.x) * 24) + 7)]));
+          conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[20] * kernel_shared[((((int)threadIdx.x) * 24) + 7)]));
+          conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[21] * kernel_shared[((((int)threadIdx.x) * 24) + 7)]));
+          conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[22] * kernel_shared[((((int)threadIdx.x) * 24) + 7)]));
+          conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[23] * kernel_shared[((((int)threadIdx.x) * 24) + 7)]));
+          conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[24] * kernel_shared[((((int)threadIdx.x) * 24) + 7)]));
+          conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[25] * kernel_shared[((((int)threadIdx.x) * 24) + 7)]));
+          conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[19] * kernel_shared[((((int)threadIdx.x) * 24) + 199)]));
+          conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[20] * kernel_shared[((((int)threadIdx.x) * 24) + 199)]));
+          conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[21] * kernel_shared[((((int)threadIdx.x) * 24) + 199)]));
+          conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[22] * kernel_shared[((((int)threadIdx.x) * 24) + 199)]));
+          conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[23] * kernel_shared[((((int)threadIdx.x) * 24) + 199)]));
+          conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[24] * kernel_shared[((((int)threadIdx.x) * 24) + 199)]));
+          conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[25] * kernel_shared[((((int)threadIdx.x) * 24) + 199)]));
+          conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[20] * kernel_shared[((((int)threadIdx.x) * 24) + 8)]));
+          conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[21] * kernel_shared[((((int)threadIdx.x) * 24) + 8)]));
+          conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[22] * kernel_shared[((((int)threadIdx.x) * 24) + 8)]));
+          conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[23] * kernel_shared[((((int)threadIdx.x) * 24) + 8)]));
+          conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[24] * kernel_shared[((((int)threadIdx.x) * 24) + 8)]));
+          conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[25] * kernel_shared[((((int)threadIdx.x) * 24) + 8)]));
+          conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[26] * kernel_shared[((((int)threadIdx.x) * 24) + 8)]));
+          conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[20] * kernel_shared[((((int)threadIdx.x) * 24) + 200)]));
+          conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[21] * kernel_shared[((((int)threadIdx.x) * 24) + 200)]));
+          conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[22] * kernel_shared[((((int)threadIdx.x) * 24) + 200)]));
+          conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[23] * kernel_shared[((((int)threadIdx.x) * 24) + 200)]));
+          conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[24] * kernel_shared[((((int)threadIdx.x) * 24) + 200)]));
+          conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[25] * kernel_shared[((((int)threadIdx.x) * 24) + 200)]));
+          conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[26] * kernel_shared[((((int)threadIdx.x) * 24) + 200)]));
+          conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[27] * kernel_shared[((((int)threadIdx.x) * 24) + 9)]));
+          conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[28] * kernel_shared[((((int)threadIdx.x) * 24) + 9)]));
+          conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[29] * kernel_shared[((((int)threadIdx.x) * 24) + 9)]));
+          conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[30] * kernel_shared[((((int)threadIdx.x) * 24) + 9)]));
+          conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[31] * kernel_shared[((((int)threadIdx.x) * 24) + 9)]));
+          conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[32] * kernel_shared[((((int)threadIdx.x) * 24) + 9)]));
+          conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[33] * kernel_shared[((((int)threadIdx.x) * 24) + 9)]));
+          conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[27] * kernel_shared[((((int)threadIdx.x) * 24) + 201)]));
+          conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[28] * kernel_shared[((((int)threadIdx.x) * 24) + 201)]));
+          conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[29] * kernel_shared[((((int)threadIdx.x) * 24) + 201)]));
+          conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[30] * kernel_shared[((((int)threadIdx.x) * 24) + 201)]));
+          conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[31] * kernel_shared[((((int)threadIdx.x) * 24) + 201)]));
+          conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[32] * kernel_shared[((((int)threadIdx.x) * 24) + 201)]));
+          conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[33] * kernel_shared[((((int)threadIdx.x) * 24) + 201)]));
+          conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[28] * kernel_shared[((((int)threadIdx.x) * 24) + 10)]));
+          conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[29] * kernel_shared[((((int)threadIdx.x) * 24) + 10)]));
+          conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[30] * kernel_shared[((((int)threadIdx.x) * 24) + 10)]));
+          conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[31] * kernel_shared[((((int)threadIdx.x) * 24) + 10)]));
+          conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[32] * kernel_shared[((((int)threadIdx.x) * 24) + 10)]));
+          conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[33] * kernel_shared[((((int)threadIdx.x) * 24) + 10)]));
+          conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[34] * kernel_shared[((((int)threadIdx.x) * 24) + 10)]));
+          conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[28] * kernel_shared[((((int)threadIdx.x) * 24) + 202)]));
+          conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[29] * kernel_shared[((((int)threadIdx.x) * 24) + 202)]));
+          conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[30] * kernel_shared[((((int)threadIdx.x) * 24) + 202)]));
+          conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[31] * kernel_shared[((((int)threadIdx.x) * 24) + 202)]));
+          conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[32] * kernel_shared[((((int)threadIdx.x) * 24) + 202)]));
+          conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[33] * kernel_shared[((((int)threadIdx.x) * 24) + 202)]));
+          conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[34] * kernel_shared[((((int)threadIdx.x) * 24) + 202)]));
+          conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[29] * kernel_shared[((((int)threadIdx.x) * 24) + 11)]));
+          conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[30] * kernel_shared[((((int)threadIdx.x) * 24) + 11)]));
+          conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[31] * kernel_shared[((((int)threadIdx.x) * 24) + 11)]));
+          conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[32] * kernel_shared[((((int)threadIdx.x) * 24) + 11)]));
+          conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[33] * kernel_shared[((((int)threadIdx.x) * 24) + 11)]));
+          conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[34] * kernel_shared[((((int)threadIdx.x) * 24) + 11)]));
+          conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[35] * kernel_shared[((((int)threadIdx.x) * 24) + 11)]));
+          conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[29] * kernel_shared[((((int)threadIdx.x) * 24) + 203)]));
+          conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[30] * kernel_shared[((((int)threadIdx.x) * 24) + 203)]));
+          conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[31] * kernel_shared[((((int)threadIdx.x) * 24) + 203)]));
+          conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[32] * kernel_shared[((((int)threadIdx.x) * 24) + 203)]));
+          conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[33] * kernel_shared[((((int)threadIdx.x) * 24) + 203)]));
+          conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[34] * kernel_shared[((((int)threadIdx.x) * 24) + 203)]));
+          conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[35] * kernel_shared[((((int)threadIdx.x) * 24) + 203)]));
+          conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[36] * kernel_shared[((((int)threadIdx.x) * 24) + 12)]));
+          conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[37] * kernel_shared[((((int)threadIdx.x) * 24) + 12)]));
+          conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[38] * kernel_shared[((((int)threadIdx.x) * 24) + 12)]));
+          conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[39] * kernel_shared[((((int)threadIdx.x) * 24) + 12)]));
+          conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[40] * kernel_shared[((((int)threadIdx.x) * 24) + 12)]));
+          conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[41] * kernel_shared[((((int)threadIdx.x) * 24) + 12)]));
+          conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[42] * kernel_shared[((((int)threadIdx.x) * 24) + 12)]));
+          conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[36] * kernel_shared[((((int)threadIdx.x) * 24) + 204)]));
+          conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[37] * kernel_shared[((((int)threadIdx.x) * 24) + 204)]));
+          conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[38] * kernel_shared[((((int)threadIdx.x) * 24) + 204)]));
+          conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[39] * kernel_shared[((((int)threadIdx.x) * 24) + 204)]));
+          conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[40] * kernel_shared[((((int)threadIdx.x) * 24) + 204)]));
+          conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[41] * kernel_shared[((((int)threadIdx.x) * 24) + 204)]));
+          conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[42] * kernel_shared[((((int)threadIdx.x) * 24) + 204)]));
+          conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[37] * kernel_shared[((((int)threadIdx.x) * 24) + 13)]));
+          conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[38] * kernel_shared[((((int)threadIdx.x) * 24) + 13)]));
+          conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[39] * kernel_shared[((((int)threadIdx.x) * 24) + 13)]));
+          conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[40] * kernel_shared[((((int)threadIdx.x) * 24) + 13)]));
+          conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[41] * kernel_shared[((((int)threadIdx.x) * 24) + 13)]));
+          conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[42] * kernel_shared[((((int)threadIdx.x) * 24) + 13)]));
+          conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[43] * kernel_shared[((((int)threadIdx.x) * 24) + 13)]));
+          conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[37] * kernel_shared[((((int)threadIdx.x) * 24) + 205)]));
+          conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[38] * kernel_shared[((((int)threadIdx.x) * 24) + 205)]));
+          conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[39] * kernel_shared[((((int)threadIdx.x) * 24) + 205)]));
+          conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[40] * kernel_shared[((((int)threadIdx.x) * 24) + 205)]));
+          conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[41] * kernel_shared[((((int)threadIdx.x) * 24) + 205)]));
+          conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[42] * kernel_shared[((((int)threadIdx.x) * 24) + 205)]));
+          conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[43] * kernel_shared[((((int)threadIdx.x) * 24) + 205)]));
+          conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[38] * kernel_shared[((((int)threadIdx.x) * 24) + 14)]));
+          conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[39] * kernel_shared[((((int)threadIdx.x) * 24) + 14)]));
+          conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[40] * kernel_shared[((((int)threadIdx.x) * 24) + 14)]));
+          conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[41] * kernel_shared[((((int)threadIdx.x) * 24) + 14)]));
+          conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[42] * kernel_shared[((((int)threadIdx.x) * 24) + 14)]));
+          conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[43] * kernel_shared[((((int)threadIdx.x) * 24) + 14)]));
+          conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[44] * kernel_shared[((((int)threadIdx.x) * 24) + 14)]));
+          conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[38] * kernel_shared[((((int)threadIdx.x) * 24) + 206)]));
+          conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[39] * kernel_shared[((((int)threadIdx.x) * 24) + 206)]));
+          conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[40] * kernel_shared[((((int)threadIdx.x) * 24) + 206)]));
+          conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[41] * kernel_shared[((((int)threadIdx.x) * 24) + 206)]));
+          conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[42] * kernel_shared[((((int)threadIdx.x) * 24) + 206)]));
+          conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[43] * kernel_shared[((((int)threadIdx.x) * 24) + 206)]));
+          conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[44] * kernel_shared[((((int)threadIdx.x) * 24) + 206)]));
+          conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[45] * kernel_shared[((((int)threadIdx.x) * 24) + 15)]));
+          conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[46] * kernel_shared[((((int)threadIdx.x) * 24) + 15)]));
+          conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[47] * kernel_shared[((((int)threadIdx.x) * 24) + 15)]));
+          conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[48] * kernel_shared[((((int)threadIdx.x) * 24) + 15)]));
+          conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[49] * kernel_shared[((((int)threadIdx.x) * 24) + 15)]));
+          conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[50] * kernel_shared[((((int)threadIdx.x) * 24) + 15)]));
+          conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[51] * kernel_shared[((((int)threadIdx.x) * 24) + 15)]));
+          conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[45] * kernel_shared[((((int)threadIdx.x) * 24) + 207)]));
+          conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[46] * kernel_shared[((((int)threadIdx.x) * 24) + 207)]));
+          conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[47] * kernel_shared[((((int)threadIdx.x) * 24) + 207)]));
+          conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[48] * kernel_shared[((((int)threadIdx.x) * 24) + 207)]));
+          conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[49] * kernel_shared[((((int)threadIdx.x) * 24) + 207)]));
+          conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[50] * kernel_shared[((((int)threadIdx.x) * 24) + 207)]));
+          conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[51] * kernel_shared[((((int)threadIdx.x) * 24) + 207)]));
+          conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[46] * kernel_shared[((((int)threadIdx.x) * 24) + 16)]));
+          conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[47] * kernel_shared[((((int)threadIdx.x) * 24) + 16)]));
+          conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[48] * kernel_shared[((((int)threadIdx.x) * 24) + 16)]));
+          conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[49] * kernel_shared[((((int)threadIdx.x) * 24) + 16)]));
+          conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[50] * kernel_shared[((((int)threadIdx.x) * 24) + 16)]));
+          conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[51] * kernel_shared[((((int)threadIdx.x) * 24) + 16)]));
+          conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[52] * kernel_shared[((((int)threadIdx.x) * 24) + 16)]));
+          conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[46] * kernel_shared[((((int)threadIdx.x) * 24) + 208)]));
+          conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[47] * kernel_shared[((((int)threadIdx.x) * 24) + 208)]));
+          conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[48] * kernel_shared[((((int)threadIdx.x) * 24) + 208)]));
+          conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[49] * kernel_shared[((((int)threadIdx.x) * 24) + 208)]));
+          conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[50] * kernel_shared[((((int)threadIdx.x) * 24) + 208)]));
+          conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[51] * kernel_shared[((((int)threadIdx.x) * 24) + 208)]));
+          conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[52] * kernel_shared[((((int)threadIdx.x) * 24) + 208)]));
+          conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[47] * kernel_shared[((((int)threadIdx.x) * 24) + 17)]));
+          conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[48] * kernel_shared[((((int)threadIdx.x) * 24) + 17)]));
+          conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[49] * kernel_shared[((((int)threadIdx.x) * 24) + 17)]));
+          conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[50] * kernel_shared[((((int)threadIdx.x) * 24) + 17)]));
+          conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[51] * kernel_shared[((((int)threadIdx.x) * 24) + 17)]));
+          conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[52] * kernel_shared[((((int)threadIdx.x) * 24) + 17)]));
+          conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[53] * kernel_shared[((((int)threadIdx.x) * 24) + 17)]));
+          conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[47] * kernel_shared[((((int)threadIdx.x) * 24) + 209)]));
+          conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[48] * kernel_shared[((((int)threadIdx.x) * 24) + 209)]));
+          conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[49] * kernel_shared[((((int)threadIdx.x) * 24) + 209)]));
+          conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[50] * kernel_shared[((((int)threadIdx.x) * 24) + 209)]));
+          conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[51] * kernel_shared[((((int)threadIdx.x) * 24) + 209)]));
+          conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[52] * kernel_shared[((((int)threadIdx.x) * 24) + 209)]));
+          conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[53] * kernel_shared[((((int)threadIdx.x) * 24) + 209)]));
+          conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[54] * kernel_shared[((((int)threadIdx.x) * 24) + 18)]));
+          conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[55] * kernel_shared[((((int)threadIdx.x) * 24) + 18)]));
+          conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[56] * kernel_shared[((((int)threadIdx.x) * 24) + 18)]));
+          conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[57] * kernel_shared[((((int)threadIdx.x) * 24) + 18)]));
+          conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[58] * kernel_shared[((((int)threadIdx.x) * 24) + 18)]));
+          conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[59] * kernel_shared[((((int)threadIdx.x) * 24) + 18)]));
+          conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[60] * kernel_shared[((((int)threadIdx.x) * 24) + 18)]));
+          conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[54] * kernel_shared[((((int)threadIdx.x) * 24) + 210)]));
+          conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[55] * kernel_shared[((((int)threadIdx.x) * 24) + 210)]));
+          conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[56] * kernel_shared[((((int)threadIdx.x) * 24) + 210)]));
+          conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[57] * kernel_shared[((((int)threadIdx.x) * 24) + 210)]));
+          conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[58] * kernel_shared[((((int)threadIdx.x) * 24) + 210)]));
+          conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[59] * kernel_shared[((((int)threadIdx.x) * 24) + 210)]));
+          conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[60] * kernel_shared[((((int)threadIdx.x) * 24) + 210)]));
+          conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[55] * kernel_shared[((((int)threadIdx.x) * 24) + 19)]));
+          conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[56] * kernel_shared[((((int)threadIdx.x) * 24) + 19)]));
+          conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[57] * kernel_shared[((((int)threadIdx.x) * 24) + 19)]));
+          conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[58] * kernel_shared[((((int)threadIdx.x) * 24) + 19)]));
+          conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[59] * kernel_shared[((((int)threadIdx.x) * 24) + 19)]));
+          conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[60] * kernel_shared[((((int)threadIdx.x) * 24) + 19)]));
+          conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[61] * kernel_shared[((((int)threadIdx.x) * 24) + 19)]));
+          conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[55] * kernel_shared[((((int)threadIdx.x) * 24) + 211)]));
+          conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[56] * kernel_shared[((((int)threadIdx.x) * 24) + 211)]));
+          conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[57] * kernel_shared[((((int)threadIdx.x) * 24) + 211)]));
+          conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[58] * kernel_shared[((((int)threadIdx.x) * 24) + 211)]));
+          conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[59] * kernel_shared[((((int)threadIdx.x) * 24) + 211)]));
+          conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[60] * kernel_shared[((((int)threadIdx.x) * 24) + 211)]));
+          conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[61] * kernel_shared[((((int)threadIdx.x) * 24) + 211)]));
+          conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[56] * kernel_shared[((((int)threadIdx.x) * 24) + 20)]));
+          conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[57] * kernel_shared[((((int)threadIdx.x) * 24) + 20)]));
+          conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[58] * kernel_shared[((((int)threadIdx.x) * 24) + 20)]));
+          conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[59] * kernel_shared[((((int)threadIdx.x) * 24) + 20)]));
+          conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[60] * kernel_shared[((((int)threadIdx.x) * 24) + 20)]));
+          conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[61] * kernel_shared[((((int)threadIdx.x) * 24) + 20)]));
+          conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[62] * kernel_shared[((((int)threadIdx.x) * 24) + 20)]));
+          conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[56] * kernel_shared[((((int)threadIdx.x) * 24) + 212)]));
+          conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[57] * kernel_shared[((((int)threadIdx.x) * 24) + 212)]));
+          conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[58] * kernel_shared[((((int)threadIdx.x) * 24) + 212)]));
+          conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[59] * kernel_shared[((((int)threadIdx.x) * 24) + 212)]));
+          conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[60] * kernel_shared[((((int)threadIdx.x) * 24) + 212)]));
+          conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[61] * kernel_shared[((((int)threadIdx.x) * 24) + 212)]));
+          conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[62] * kernel_shared[((((int)threadIdx.x) * 24) + 212)]));
+          conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[63] * kernel_shared[((((int)threadIdx.x) * 24) + 21)]));
+          conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[64] * kernel_shared[((((int)threadIdx.x) * 24) + 21)]));
+          conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[65] * kernel_shared[((((int)threadIdx.x) * 24) + 21)]));
+          conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[66] * kernel_shared[((((int)threadIdx.x) * 24) + 21)]));
+          conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[67] * kernel_shared[((((int)threadIdx.x) * 24) + 21)]));
+          conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[68] * kernel_shared[((((int)threadIdx.x) * 24) + 21)]));
+          conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[69] * kernel_shared[((((int)threadIdx.x) * 24) + 21)]));
+          conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[63] * kernel_shared[((((int)threadIdx.x) * 24) + 213)]));
+          conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[64] * kernel_shared[((((int)threadIdx.x) * 24) + 213)]));
+          conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[65] * kernel_shared[((((int)threadIdx.x) * 24) + 213)]));
+          conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[66] * kernel_shared[((((int)threadIdx.x) * 24) + 213)]));
+          conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[67] * kernel_shared[((((int)threadIdx.x) * 24) + 213)]));
+          conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[68] * kernel_shared[((((int)threadIdx.x) * 24) + 213)]));
+          conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[69] * kernel_shared[((((int)threadIdx.x) * 24) + 213)]));
+          conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[64] * kernel_shared[((((int)threadIdx.x) * 24) + 22)]));
+          conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[65] * kernel_shared[((((int)threadIdx.x) * 24) + 22)]));
+          conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[66] * kernel_shared[((((int)threadIdx.x) * 24) + 22)]));
+          conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[67] * kernel_shared[((((int)threadIdx.x) * 24) + 22)]));
+          conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[68] * kernel_shared[((((int)threadIdx.x) * 24) + 22)]));
+          conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[69] * kernel_shared[((((int)threadIdx.x) * 24) + 22)]));
+          conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[70] * kernel_shared[((((int)threadIdx.x) * 24) + 22)]));
+          conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[64] * kernel_shared[((((int)threadIdx.x) * 24) + 214)]));
+          conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[65] * kernel_shared[((((int)threadIdx.x) * 24) + 214)]));
+          conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[66] * kernel_shared[((((int)threadIdx.x) * 24) + 214)]));
+          conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[67] * kernel_shared[((((int)threadIdx.x) * 24) + 214)]));
+          conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[68] * kernel_shared[((((int)threadIdx.x) * 24) + 214)]));
+          conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[69] * kernel_shared[((((int)threadIdx.x) * 24) + 214)]));
+          conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[70] * kernel_shared[((((int)threadIdx.x) * 24) + 214)]));
+          conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[65] * kernel_shared[((((int)threadIdx.x) * 24) + 23)]));
+          conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[66] * kernel_shared[((((int)threadIdx.x) * 24) + 23)]));
+          conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[67] * kernel_shared[((((int)threadIdx.x) * 24) + 23)]));
+          conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[68] * kernel_shared[((((int)threadIdx.x) * 24) + 23)]));
+          conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[69] * kernel_shared[((((int)threadIdx.x) * 24) + 23)]));
+          conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[70] * kernel_shared[((((int)threadIdx.x) * 24) + 23)]));
+          conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[71] * kernel_shared[((((int)threadIdx.x) * 24) + 23)]));
+          conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[65] * kernel_shared[((((int)threadIdx.x) * 24) + 215)]));
+          conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[66] * kernel_shared[((((int)threadIdx.x) * 24) + 215)]));
+          conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[67] * kernel_shared[((((int)threadIdx.x) * 24) + 215)]));
+          conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[68] * kernel_shared[((((int)threadIdx.x) * 24) + 215)]));
+          conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[69] * kernel_shared[((((int)threadIdx.x) * 24) + 215)]));
+          conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[70] * kernel_shared[((((int)threadIdx.x) * 24) + 215)]));
+          conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[71] * kernel_shared[((((int)threadIdx.x) * 24) + 215)]));
         }
       }
-      for (int i1_inner = 0; i1_inner < 4; ++i1_inner) {
-        compute[((((((int)blockIdx.x) * 784) + ((((int)threadIdx.x) / 7) * 196)) + (i1_inner * 49)) + (((int)threadIdx.x) % 7))] = max((conv2d_nchw[i1_inner] + bias[(((((int)blockIdx.x) * 16) + ((((int)threadIdx.x) / 7) * 4)) + i1_inner)]), 0.000000e+00f);
-        compute[(((((((int)blockIdx.x) * 784) + ((((int)threadIdx.x) / 7) * 196)) + (i1_inner * 49)) + (((int)threadIdx.x) % 7)) + 7)] = max((conv2d_nchw[(i1_inner + 4)] + bias[(((((int)blockIdx.x) * 16) + ((((int)threadIdx.x) / 7) * 4)) + i1_inner)]), 0.000000e+00f);
-        compute[(((((((int)blockIdx.x) * 784) + ((((int)threadIdx.x) / 7) * 196)) + (i1_inner * 49)) + (((int)threadIdx.x) % 7)) + 14)] = max((conv2d_nchw[(i1_inner + 8)] + bias[(((((int)blockIdx.x) * 16) + ((((int)threadIdx.x) / 7) * 4)) + i1_inner)]), 0.000000e+00f);
-        compute[(((((((int)blockIdx.x) * 784) + ((((int)threadIdx.x) / 7) * 196)) + (i1_inner * 49)) + (((int)threadIdx.x) % 7)) + 21)] = max((conv2d_nchw[(i1_inner + 12)] + bias[(((((int)blockIdx.x) * 16) + ((((int)threadIdx.x) / 7) * 4)) + i1_inner)]), 0.000000e+00f);
-        compute[(((((((int)blockIdx.x) * 784) + ((((int)threadIdx.x) / 7) * 196)) + (i1_inner * 49)) + (((int)threadIdx.x) % 7)) + 28)] = max((conv2d_nchw[(i1_inner + 16)] + bias[(((((int)blockIdx.x) * 16) + ((((int)threadIdx.x) / 7) * 4)) + i1_inner)]), 0.000000e+00f);
-        compute[(((((((int)blockIdx.x) * 784) + ((((int)threadIdx.x) / 7) * 196)) + (i1_inner * 49)) + (((int)threadIdx.x) % 7)) + 35)] = max((conv2d_nchw[(i1_inner + 20)] + bias[(((((int)blockIdx.x) * 16) + ((((int)threadIdx.x) / 7) * 4)) + i1_inner)]), 0.000000e+00f);
-        compute[(((((((int)blockIdx.x) * 784) + ((((int)threadIdx.x) / 7) * 196)) + (i1_inner * 49)) + (((int)threadIdx.x) % 7)) + 42)] = max((conv2d_nchw[(i1_inner + 24)] + bias[(((((int)blockIdx.x) * 16) + ((((int)threadIdx.x) / 7) * 4)) + i1_inner)]), 0.000000e+00f);
-      }
+      compute[((((((int)blockIdx.x) / 7) * 784) + (((int)threadIdx.x) * 49)) + ((((int)blockIdx.x) % 7) * 7))] = max((conv2d_nchw[0] + bias[(((((int)blockIdx.x) / 7) * 16) + ((int)threadIdx.x))]), 0.000000e+00f);
+      compute[(((((((int)blockIdx.x) / 7) * 784) + (((int)threadIdx.x) * 49)) + ((((int)blockIdx.x) % 7) * 7)) + 1)] = max((conv2d_nchw[1] + bias[(((((int)blockIdx.x) / 7) * 16) + ((int)threadIdx.x))]), 0.000000e+00f);
+      compute[(((((((int)blockIdx.x) / 7) * 784) + (((int)threadIdx.x) * 49)) + ((((int)blockIdx.x) % 7) * 7)) + 2)] = max((conv2d_nchw[2] + bias[(((((int)blockIdx.x) / 7) * 16) + ((int)threadIdx.x))]), 0.000000e+00f);
+      compute[(((((((int)blockIdx.x) / 7) * 784) + (((int)threadIdx.x) * 49)) + ((((int)blockIdx.x) % 7) * 7)) + 3)] = max((conv2d_nchw[3] + bias[(((((int)blockIdx.x) / 7) * 16) + ((int)threadIdx.x))]), 0.000000e+00f);
+      compute[(((((((int)blockIdx.x) / 7) * 784) + (((int)threadIdx.x) * 49)) + ((((int)blockIdx.x) % 7) * 7)) + 4)] = max((conv2d_nchw[4] + bias[(((((int)blockIdx.x) / 7) * 16) + ((int)threadIdx.x))]), 0.000000e+00f);
+      compute[(((((((int)blockIdx.x) / 7) * 784) + (((int)threadIdx.x) * 49)) + ((((int)blockIdx.x) % 7) * 7)) + 5)] = max((conv2d_nchw[5] + bias[(((((int)blockIdx.x) / 7) * 16) + ((int)threadIdx.x))]), 0.000000e+00f);
+      compute[(((((((int)blockIdx.x) / 7) * 784) + (((int)threadIdx.x) * 49)) + ((((int)blockIdx.x) % 7) * 7)) + 6)] = max((conv2d_nchw[6] + bias[(((((int)blockIdx.x) / 7) * 16) + ((int)threadIdx.x))]), 0.000000e+00f);
+      compute[(((((((int)blockIdx.x) / 7) * 784) + (((int)threadIdx.x) * 49)) + ((((int)blockIdx.x) % 7) * 7)) + 392)] = max((conv2d_nchw[7] + bias[((((((int)blockIdx.x) / 7) * 16) + ((int)threadIdx.x)) + 8)]), 0.000000e+00f);
+      compute[(((((((int)blockIdx.x) / 7) * 784) + (((int)threadIdx.x) * 49)) + ((((int)blockIdx.x) % 7) * 7)) + 393)] = max((conv2d_nchw[8] + bias[((((((int)blockIdx.x) / 7) * 16) + ((int)threadIdx.x)) + 8)]), 0.000000e+00f);
+      compute[(((((((int)blockIdx.x) / 7) * 784) + (((int)threadIdx.x) * 49)) + ((((int)blockIdx.x) % 7) * 7)) + 394)] = max((conv2d_nchw[9] + bias[((((((int)blockIdx.x) / 7) * 16) + ((int)threadIdx.x)) + 8)]), 0.000000e+00f);
+      compute[(((((((int)blockIdx.x) / 7) * 784) + (((int)threadIdx.x) * 49)) + ((((int)blockIdx.x) % 7) * 7)) + 395)] = max((conv2d_nchw[10] + bias[((((((int)blockIdx.x) / 7) * 16) + ((int)threadIdx.x)) + 8)]), 0.000000e+00f);
+      compute[(((((((int)blockIdx.x) / 7) * 784) + (((int)threadIdx.x) * 49)) + ((((int)blockIdx.x) % 7) * 7)) + 396)] = max((conv2d_nchw[11] + bias[((((((int)blockIdx.x) / 7) * 16) + ((int)threadIdx.x)) + 8)]), 0.000000e+00f);
+      compute[(((((((int)blockIdx.x) / 7) * 784) + (((int)threadIdx.x) * 49)) + ((((int)blockIdx.x) % 7) * 7)) + 397)] = max((conv2d_nchw[12] + bias[((((((int)blockIdx.x) / 7) * 16) + ((int)threadIdx.x)) + 8)]), 0.000000e+00f);
+      compute[(((((((int)blockIdx.x) / 7) * 784) + (((int)threadIdx.x) * 49)) + ((((int)blockIdx.x) % 7) * 7)) + 398)] = max((conv2d_nchw[13] + bias[((((((int)blockIdx.x) / 7) * 16) + ((int)threadIdx.x)) + 8)]), 0.000000e+00f);
     }
 
 
@@ -1098,7 +1370,7 @@ In the example below we resume the status and do more 5 trials.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 2 minutes  20.154 seconds)
+   **Total running time of the script:** ( 2 minutes  21.500 seconds)
 
 
 .. _sphx_glr_download_how_to_tune_with_autoscheduler_tune_conv2d_layer_cuda.py:
diff --git a/docs/_sources/how_to/tune_with_autoscheduler/tune_network_cuda.rst.txt b/docs/_sources/how_to/tune_with_autoscheduler/tune_network_cuda.rst.txt
index 4ea38d9b8..2e7b46341 100644
--- a/docs/_sources/how_to/tune_with_autoscheduler/tune_network_cuda.rst.txt
+++ b/docs/_sources/how_to/tune_with_autoscheduler/tune_network_cuda.rst.txt
@@ -614,7 +614,7 @@ so we can read the log file and load the best schedules.
     Evaluate inference time cost...
     Execution time summary:
      mean (ms)   median (ms)    max (ms)     min (ms)     std (ms)  
-       9.8233       9.8322       9.8639       9.7739       0.0372   
+       9.8680       9.8876       9.9006       9.8159       0.0372   
                
 
 
diff --git a/docs/_sources/how_to/tune_with_autoscheduler/tune_network_x86.rst.txt b/docs/_sources/how_to/tune_with_autoscheduler/tune_network_x86.rst.txt
index 5857df18b..64e279ebf 100644
--- a/docs/_sources/how_to/tune_with_autoscheduler/tune_network_x86.rst.txt
+++ b/docs/_sources/how_to/tune_with_autoscheduler/tune_network_x86.rst.txt
@@ -633,7 +633,7 @@ so we can read the log file and load the best schedules.
     Evaluate inference time cost...
     Execution time summary:
      mean (ms)   median (ms)    max (ms)     min (ms)     std (ms)  
-      755.6295     751.7929     764.4012     750.6943      6.2188   
+      769.9250     770.6566     774.9276     764.1909      4.4136   
                
 
 
@@ -658,7 +658,7 @@ Other Tips
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 1 minutes  18.957 seconds)
+   **Total running time of the script:** ( 1 minutes  21.317 seconds)
 
 
 .. _sphx_glr_download_how_to_tune_with_autoscheduler_tune_network_x86.py:
diff --git a/docs/_sources/how_to/tune_with_autoscheduler/tune_sparse_x86.rst.txt b/docs/_sources/how_to/tune_with_autoscheduler/tune_sparse_x86.rst.txt
index 2a4d835fb..8493cb6fe 100644
--- a/docs/_sources/how_to/tune_with_autoscheduler/tune_sparse_x86.rst.txt
+++ b/docs/_sources/how_to/tune_with_autoscheduler/tune_sparse_x86.rst.txt
@@ -362,407 +362,77 @@ layout transformation, parallelization, vectorization, unrolling, and operator f
                  placeholder_4: Buffer(placeholder_14: Pointer(float32), float32, [65536], []),
                  compute: Buffer(compute_2: Pointer(float32), float32, [65536], [])}
       buffer_map = {placeholder_5: placeholder, placeholder_6: placeholder_1, placeholder_7: placeholder_2, placeholder_8: placeholder_3, placeholder_9: placeholder_4, compute_1: compute} {
-      for (i0.outer: int32, 0, 16) "parallel" {
-        allocate(compute_3: Pointer(global float32), float32, [256]), storage_scope = global;
-        for (i1.outer: int32, 0, 16) {
-          for (nb_j.inner: int32, 0, 2) {
-            let cse_var_2: int32 = (nb_j.inner*16)
-            let cse_var_1: int32 = ((i1.outer*2) + nb_j.inner)
-             {
-              compute_4: Buffer(compute_3, float32, [256], [])[cse_var_2] = 0f32
-              compute_4[(cse_var_2 + 1)] = 0f32
-              compute_4[(cse_var_2 + 2)] = 0f32
-              compute_4[(cse_var_2 + 3)] = 0f32
-              compute_4[(cse_var_2 + 4)] = 0f32
-              compute_4[(cse_var_2 + 5)] = 0f32
-              compute_4[(cse_var_2 + 6)] = 0f32
-              compute_4[(cse_var_2 + 7)] = 0f32
-              compute_4[(cse_var_2 + 8)] = 0f32
-              compute_4[(cse_var_2 + 9)] = 0f32
-              compute_4[(cse_var_2 + 10)] = 0f32
-              compute_4[(cse_var_2 + 11)] = 0f32
-              compute_4[(cse_var_2 + 12)] = 0f32
-              compute_4[(cse_var_2 + 13)] = 0f32
-              compute_4[(cse_var_2 + 14)] = 0f32
-              compute_4[(cse_var_2 + 15)] = 0f32
-              compute_4[(cse_var_2 + 32)] = 0f32
-              compute_4[(cse_var_2 + 33)] = 0f32
-              compute_4[(cse_var_2 + 34)] = 0f32
-              compute_4[(cse_var_2 + 35)] = 0f32
-              compute_4[(cse_var_2 + 36)] = 0f32
-              compute_4[(cse_var_2 + 37)] = 0f32
-              compute_4[(cse_var_2 + 38)] = 0f32
-              compute_4[(cse_var_2 + 39)] = 0f32
-              compute_4[(cse_var_2 + 40)] = 0f32
-              compute_4[(cse_var_2 + 41)] = 0f32
-              compute_4[(cse_var_2 + 42)] = 0f32
-              compute_4[(cse_var_2 + 43)] = 0f32
-              compute_4[(cse_var_2 + 44)] = 0f32
-              compute_4[(cse_var_2 + 45)] = 0f32
-              compute_4[(cse_var_2 + 46)] = 0f32
-              compute_4[(cse_var_2 + 47)] = 0f32
-              compute_4[(cse_var_2 + 64)] = 0f32
-              compute_4[(cse_var_2 + 65)] = 0f32
-              compute_4[(cse_var_2 + 66)] = 0f32
-              compute_4[(cse_var_2 + 67)] = 0f32
-              compute_4[(cse_var_2 + 68)] = 0f32
-              compute_4[(cse_var_2 + 69)] = 0f32
-              compute_4[(cse_var_2 + 70)] = 0f32
-              compute_4[(cse_var_2 + 71)] = 0f32
-              compute_4[(cse_var_2 + 72)] = 0f32
-              compute_4[(cse_var_2 + 73)] = 0f32
-              compute_4[(cse_var_2 + 74)] = 0f32
-              compute_4[(cse_var_2 + 75)] = 0f32
-              compute_4[(cse_var_2 + 76)] = 0f32
-              compute_4[(cse_var_2 + 77)] = 0f32
-              compute_4[(cse_var_2 + 78)] = 0f32
-              compute_4[(cse_var_2 + 79)] = 0f32
-              compute_4[(cse_var_2 + 96)] = 0f32
-              compute_4[(cse_var_2 + 97)] = 0f32
-              compute_4[(cse_var_2 + 98)] = 0f32
-              compute_4[(cse_var_2 + 99)] = 0f32
-              compute_4[(cse_var_2 + 100)] = 0f32
-              compute_4[(cse_var_2 + 101)] = 0f32
-              compute_4[(cse_var_2 + 102)] = 0f32
-              compute_4[(cse_var_2 + 103)] = 0f32
-              compute_4[(cse_var_2 + 104)] = 0f32
-              compute_4[(cse_var_2 + 105)] = 0f32
-              compute_4[(cse_var_2 + 106)] = 0f32
-              compute_4[(cse_var_2 + 107)] = 0f32
-              compute_4[(cse_var_2 + 108)] = 0f32
-              compute_4[(cse_var_2 + 109)] = 0f32
-              compute_4[(cse_var_2 + 110)] = 0f32
-              compute_4[(cse_var_2 + 111)] = 0f32
-              compute_4[(cse_var_2 + 128)] = 0f32
-              compute_4[(cse_var_2 + 129)] = 0f32
-              compute_4[(cse_var_2 + 130)] = 0f32
-              compute_4[(cse_var_2 + 131)] = 0f32
-              compute_4[(cse_var_2 + 132)] = 0f32
-              compute_4[(cse_var_2 + 133)] = 0f32
-              compute_4[(cse_var_2 + 134)] = 0f32
-              compute_4[(cse_var_2 + 135)] = 0f32
-              compute_4[(cse_var_2 + 136)] = 0f32
-              compute_4[(cse_var_2 + 137)] = 0f32
-              compute_4[(cse_var_2 + 138)] = 0f32
-              compute_4[(cse_var_2 + 139)] = 0f32
-              compute_4[(cse_var_2 + 140)] = 0f32
-              compute_4[(cse_var_2 + 141)] = 0f32
-              compute_4[(cse_var_2 + 142)] = 0f32
-              compute_4[(cse_var_2 + 143)] = 0f32
-              compute_4[(cse_var_2 + 160)] = 0f32
-              compute_4[(cse_var_2 + 161)] = 0f32
-              compute_4[(cse_var_2 + 162)] = 0f32
-              compute_4[(cse_var_2 + 163)] = 0f32
-              compute_4[(cse_var_2 + 164)] = 0f32
-              compute_4[(cse_var_2 + 165)] = 0f32
-              compute_4[(cse_var_2 + 166)] = 0f32
-              compute_4[(cse_var_2 + 167)] = 0f32
-              compute_4[(cse_var_2 + 168)] = 0f32
-              compute_4[(cse_var_2 + 169)] = 0f32
-              compute_4[(cse_var_2 + 170)] = 0f32
-              compute_4[(cse_var_2 + 171)] = 0f32
-              compute_4[(cse_var_2 + 172)] = 0f32
-              compute_4[(cse_var_2 + 173)] = 0f32
-              compute_4[(cse_var_2 + 174)] = 0f32
-              compute_4[(cse_var_2 + 175)] = 0f32
-              compute_4[(cse_var_2 + 192)] = 0f32
-              compute_4[(cse_var_2 + 193)] = 0f32
-              compute_4[(cse_var_2 + 194)] = 0f32
-              compute_4[(cse_var_2 + 195)] = 0f32
-              compute_4[(cse_var_2 + 196)] = 0f32
-              compute_4[(cse_var_2 + 197)] = 0f32
-              compute_4[(cse_var_2 + 198)] = 0f32
-              compute_4[(cse_var_2 + 199)] = 0f32
-              compute_4[(cse_var_2 + 200)] = 0f32
-              compute_4[(cse_var_2 + 201)] = 0f32
-              compute_4[(cse_var_2 + 202)] = 0f32
-              compute_4[(cse_var_2 + 203)] = 0f32
-              compute_4[(cse_var_2 + 204)] = 0f32
-              compute_4[(cse_var_2 + 205)] = 0f32
-              compute_4[(cse_var_2 + 206)] = 0f32
-              compute_4[(cse_var_2 + 207)] = 0f32
-              compute_4[(cse_var_2 + 224)] = 0f32
-              compute_4[(cse_var_2 + 225)] = 0f32
-              compute_4[(cse_var_2 + 226)] = 0f32
-              compute_4[(cse_var_2 + 227)] = 0f32
-              compute_4[(cse_var_2 + 228)] = 0f32
-              compute_4[(cse_var_2 + 229)] = 0f32
-              compute_4[(cse_var_2 + 230)] = 0f32
-              compute_4[(cse_var_2 + 231)] = 0f32
-              compute_4[(cse_var_2 + 232)] = 0f32
-              compute_4[(cse_var_2 + 233)] = 0f32
-              compute_4[(cse_var_2 + 234)] = 0f32
-              compute_4[(cse_var_2 + 235)] = 0f32
-              compute_4[(cse_var_2 + 236)] = 0f32
-              compute_4[(cse_var_2 + 237)] = 0f32
-              compute_4[(cse_var_2 + 238)] = 0f32
-              compute_4[(cse_var_2 + 239)] = 0f32
-              for (elem_idx: int32, 0, (placeholder_3[(cse_var_1 + 1)] - placeholder_3[cse_var_1])) {
-                let cse_var_131: int32 = (cse_var_2 + 143)
-                let cse_var_130: int32 = (cse_var_2 + 15)
-                let cse_var_129: int32 = (cse_var_2 + 160)
-                let cse_var_128: int32 = (cse_var_2 + 161)
-                let cse_var_127: int32 = (cse_var_2 + 162)
-                let cse_var_126: int32 = (cse_var_2 + 163)
-                let cse_var_125: int32 = (cse_var_2 + 164)
-                let cse_var_124: int32 = (cse_var_2 + 165)
-                let cse_var_123: int32 = (cse_var_2 + 166)
-                let cse_var_122: int32 = (cse_var_2 + 167)
-                let cse_var_121: int32 = (cse_var_2 + 168)
-                let cse_var_120: int32 = (cse_var_2 + 169)
-                let cse_var_119: int32 = (cse_var_2 + 170)
-                let cse_var_118: int32 = (cse_var_2 + 171)
-                let cse_var_117: int32 = (cse_var_2 + 172)
-                let cse_var_116: int32 = (cse_var_2 + 1)
-                let cse_var_115: int32 = (cse_var_2 + 174)
-                let cse_var_114: int32 = (cse_var_2 + 175)
-                let cse_var_113: int32 = (cse_var_2 + 192)
-                let cse_var_112: int32 = (cse_var_2 + 193)
-                let cse_var_111: int32 = (cse_var_2 + 194)
-                let cse_var_110: int32 = (cse_var_2 + 195)
-                let cse_var_109: int32 = (cse_var_2 + 196)
-                let cse_var_108: int32 = (cse_var_2 + 197)
-                let cse_var_107: int32 = (cse_var_2 + 198)
-                let cse_var_106: int32 = (cse_var_2 + 199)
-                let cse_var_105: int32 = (cse_var_2 + 2)
-                let cse_var_104: int32 = (cse_var_2 + 200)
-                let cse_var_103: int32 = (cse_var_2 + 201)
-                let cse_var_102: int32 = (cse_var_2 + 202)
-                let cse_var_101: int32 = (cse_var_2 + 203)
-                let cse_var_100: int32 = (cse_var_2 + 173)
-                let cse_var_99: int32 = (cse_var_2 + 10)
-                let cse_var_98: int32 = (cse_var_2 + 100)
-                let cse_var_97: int32 = (cse_var_2 + 101)
-                let cse_var_96: int32 = (cse_var_2 + 102)
-                let cse_var_95: int32 = (cse_var_2 + 103)
-                let cse_var_94: int32 = (cse_var_2 + 104)
-                let cse_var_93: int32 = (cse_var_2 + 105)
-                let cse_var_92: int32 = (cse_var_2 + 106)
-                let cse_var_91: int32 = (cse_var_2 + 107)
-                let cse_var_90: int32 = (cse_var_2 + 108)
-                let cse_var_89: int32 = (cse_var_2 + 109)
-                let cse_var_88: int32 = (cse_var_2 + 11)
-                let cse_var_87: int32 = (cse_var_2 + 110)
-                let cse_var_86: int32 = (cse_var_2 + 111)
-                let cse_var_85: int32 = (cse_var_2 + 12)
-                let cse_var_84: int32 = (cse_var_2 + 142)
-                let cse_var_83: int32 = (cse_var_2 + 129)
-                let cse_var_82: int32 = (cse_var_2 + 13)
-                let cse_var_81: int32 = (cse_var_2 + 130)
-                let cse_var_80: int32 = (cse_var_2 + 131)
-                let cse_var_79: int32 = (cse_var_2 + 132)
-                let cse_var_78: int32 = (cse_var_2 + 133)
-                let cse_var_77: int32 = (cse_var_2 + 134)
-                let cse_var_76: int32 = (cse_var_2 + 135)
-                let cse_var_75: int32 = (cse_var_2 + 136)
-                let cse_var_74: int32 = (cse_var_2 + 137)
-                let cse_var_73: int32 = (cse_var_2 + 138)
-                let cse_var_72: int32 = (cse_var_2 + 139)
-                let cse_var_71: int32 = (cse_var_2 + 14)
-                let cse_var_70: int32 = (cse_var_2 + 140)
-                let cse_var_69: int32 = (cse_var_2 + 141)
-                let cse_var_68: int32 = (cse_var_2 + 128)
-                let cse_var_67: int32 = (cse_var_2 + 44)
-                let cse_var_66: int32 = (cse_var_2 + 45)
-                let cse_var_65: int32 = (cse_var_2 + 46)
-                let cse_var_64: int32 = (cse_var_2 + 47)
-                let cse_var_63: int32 = (cse_var_2 + 5)
-                let cse_var_62: int32 = (cse_var_2 + 6)
-                let cse_var_61: int32 = (cse_var_2 + 64)
-                let cse_var_60: int32 = (cse_var_2 + 65)
-                let cse_var_59: int32 = (cse_var_2 + 66)
-                let cse_var_58: int32 = (cse_var_2 + 67)
-                let cse_var_57: int32 = (cse_var_2 + 68)
-                let cse_var_56: int32 = (cse_var_2 + 69)
-                let cse_var_55: int32 = (cse_var_2 + 7)
-                let cse_var_54: int32 = (cse_var_2 + 70)
-                let cse_var_53: int32 = (cse_var_2 + 71)
-                let cse_var_52: int32 = (cse_var_2 + 204)
-                let cse_var_51: int32 = (cse_var_2 + 73)
-                let cse_var_50: int32 = (cse_var_2 + 74)
-                let cse_var_49: int32 = (cse_var_2 + 75)
-                let cse_var_48: int32 = (cse_var_2 + 76)
-                let cse_var_47: int32 = (cse_var_2 + 77)
-                let cse_var_46: int32 = (cse_var_2 + 78)
-                let cse_var_45: int32 = (cse_var_2 + 79)
-                let cse_var_44: int32 = (cse_var_2 + 8)
-                let cse_var_43: int32 = (cse_var_2 + 9)
-                let cse_var_42: int32 = (cse_var_2 + 96)
-                let cse_var_41: int32 = (cse_var_2 + 97)
-                let cse_var_40: int32 = (cse_var_2 + 98)
-                let cse_var_39: int32 = (cse_var_2 + 99)
-                let cse_var_38: int32 = (elem_idx*16)
-                let cse_var_37: int32 = (i0.outer*2048)
-                let cse_var_36: int32 = (cse_var_2 + 72)
-                let cse_var_35: int32 = (cse_var_2 + 205)
-                let cse_var_34: int32 = (cse_var_2 + 206)
-                let cse_var_33: int32 = (cse_var_2 + 207)
-                let cse_var_32: int32 = (cse_var_2 + 224)
-                let cse_var_31: int32 = (cse_var_2 + 225)
-                let cse_var_30: int32 = (cse_var_2 + 226)
-                let cse_var_29: int32 = (cse_var_2 + 227)
-                let cse_var_28: int32 = (cse_var_2 + 228)
-                let cse_var_27: int32 = (cse_var_2 + 229)
-                let cse_var_26: int32 = (cse_var_2 + 230)
-                let cse_var_25: int32 = (cse_var_2 + 231)
-                let cse_var_24: int32 = (cse_var_2 + 232)
-                let cse_var_23: int32 = (cse_var_2 + 233)
-                let cse_var_22: int32 = (cse_var_2 + 234)
-                let cse_var_21: int32 = (cse_var_2 + 235)
-                let cse_var_20: int32 = (cse_var_2 + 43)
-                let cse_var_19: int32 = (cse_var_2 + 42)
-                let cse_var_18: int32 = (cse_var_2 + 41)
-                let cse_var_17: int32 = (cse_var_2 + 40)
-                let cse_var_16: int32 = (cse_var_2 + 4)
-                let cse_var_15: int32 = (cse_var_2 + 39)
-                let cse_var_14: int32 = (cse_var_2 + 38)
-                let cse_var_13: int32 = (cse_var_2 + 37)
-                let cse_var_12: int32 = (cse_var_2 + 236)
-                let cse_var_11: int32 = (cse_var_2 + 35)
-                let cse_var_10: int32 = (cse_var_2 + 34)
-                let cse_var_9: int32 = (cse_var_2 + 33)
-                let cse_var_8: int32 = (cse_var_2 + 32)
-                let cse_var_7: int32 = (cse_var_2 + 3)
-                let cse_var_6: int32 = (cse_var_2 + 239)
-                let cse_var_5: int32 = (cse_var_2 + 238)
-                let cse_var_4: int32 = (cse_var_2 + 237)
-                let cse_var_3: int32 = (cse_var_2 + 36)
+      for (i0.outer.i1.outer.fused: int32, 0, 32) "parallel" {
+        allocate(compute_3: Pointer(global float32), float32, [2048]), storage_scope = global {
+          for (i.outer.inner: int32, 0, 4) {
+            for (nb_j.inner: int32, 0, 2) {
+              for (i.inner.init: int32, 0, 16) {
+                let cse_var_1: int32 = (((i.outer.inner*512) + (i.inner.init*32)) + (nb_j.inner*16))
                  {
-                  compute_4[cse_var_2] = (compute_4[cse_var_2] + (placeholder_1[((placeholder_3[cse_var_1]*16) + cse_var_38)]*max(placeholder[(cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)])], 0f32)))
-                  compute_4[cse_var_116] = (compute_4[cse_var_116] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 1)]*max(placeholder[(cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)])], 0f32)))
-                  compute_4[cse_var_105] = (compute_4[cse_var_105] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 2)]*max(placeholder[(cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)])], 0f32)))
-                  compute_4[cse_var_7] = (compute_4[cse_var_7] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 3)]*max(placeholder[(cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)])], 0f32)))
-                  compute_4[cse_var_16] = (compute_4[cse_var_16] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 4)]*max(placeholder[(cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)])], 0f32)))
-                  compute_4[cse_var_63] = (compute_4[cse_var_63] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 5)]*max(placeholder[(cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)])], 0f32)))
-                  compute_4[cse_var_62] = (compute_4[cse_var_62] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 6)]*max(placeholder[(cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)])], 0f32)))
-                  compute_4[cse_var_55] = (compute_4[cse_var_55] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 7)]*max(placeholder[(cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)])], 0f32)))
-                  compute_4[cse_var_44] = (compute_4[cse_var_44] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 8)]*max(placeholder[(cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)])], 0f32)))
-                  compute_4[cse_var_43] = (compute_4[cse_var_43] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 9)]*max(placeholder[(cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)])], 0f32)))
-                  compute_4[cse_var_99] = (compute_4[cse_var_99] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 10)]*max(placeholder[(cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)])], 0f32)))
-                  compute_4[cse_var_88] = (compute_4[cse_var_88] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 11)]*max(placeholder[(cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)])], 0f32)))
-                  compute_4[cse_var_85] = (compute_4[cse_var_85] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 12)]*max(placeholder[(cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)])], 0f32)))
-                  compute_4[cse_var_82] = (compute_4[cse_var_82] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 13)]*max(placeholder[(cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)])], 0f32)))
-                  compute_4[cse_var_71] = (compute_4[cse_var_71] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 14)]*max(placeholder[(cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)])], 0f32)))
-                  compute_4[cse_var_130] = (compute_4[cse_var_130] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 15)]*max(placeholder[(cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)])], 0f32)))
-                  compute_4[cse_var_8] = (compute_4[cse_var_8] + (placeholder_1[((placeholder_3[cse_var_1]*16) + cse_var_38)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 256)], 0f32)))
-                  compute_4[cse_var_9] = (compute_4[cse_var_9] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 1)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 256)], 0f32)))
-                  compute_4[cse_var_10] = (compute_4[cse_var_10] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 2)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 256)], 0f32)))
-                  compute_4[cse_var_11] = (compute_4[cse_var_11] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 3)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 256)], 0f32)))
-                  compute_4[cse_var_3] = (compute_4[cse_var_3] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 4)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 256)], 0f32)))
-                  compute_4[cse_var_13] = (compute_4[cse_var_13] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 5)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 256)], 0f32)))
-                  compute_4[cse_var_14] = (compute_4[cse_var_14] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 6)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 256)], 0f32)))
-                  compute_4[cse_var_15] = (compute_4[cse_var_15] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 7)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 256)], 0f32)))
-                  compute_4[cse_var_17] = (compute_4[cse_var_17] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 8)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 256)], 0f32)))
-                  compute_4[cse_var_18] = (compute_4[cse_var_18] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 9)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 256)], 0f32)))
-                  compute_4[cse_var_19] = (compute_4[cse_var_19] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 10)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 256)], 0f32)))
-                  compute_4[cse_var_20] = (compute_4[cse_var_20] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 11)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 256)], 0f32)))
-                  compute_4[cse_var_67] = (compute_4[cse_var_67] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 12)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 256)], 0f32)))
-                  compute_4[cse_var_66] = (compute_4[cse_var_66] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 13)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 256)], 0f32)))
-                  compute_4[cse_var_65] = (compute_4[cse_var_65] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 14)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 256)], 0f32)))
-                  compute_4[cse_var_64] = (compute_4[cse_var_64] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 15)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 256)], 0f32)))
-                  compute_4[cse_var_61] = (compute_4[cse_var_61] + (placeholder_1[((placeholder_3[cse_var_1]*16) + cse_var_38)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 512)], 0f32)))
-                  compute_4[cse_var_60] = (compute_4[cse_var_60] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 1)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 512)], 0f32)))
-                  compute_4[cse_var_59] = (compute_4[cse_var_59] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 2)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 512)], 0f32)))
-                  compute_4[cse_var_58] = (compute_4[cse_var_58] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 3)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 512)], 0f32)))
-                  compute_4[cse_var_57] = (compute_4[cse_var_57] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 4)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 512)], 0f32)))
-                  compute_4[cse_var_56] = (compute_4[cse_var_56] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 5)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 512)], 0f32)))
-                  compute_4[cse_var_54] = (compute_4[cse_var_54] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 6)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 512)], 0f32)))
-                  compute_4[cse_var_53] = (compute_4[cse_var_53] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 7)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 512)], 0f32)))
-                  compute_4[cse_var_36] = (compute_4[cse_var_36] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 8)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 512)], 0f32)))
-                  compute_4[cse_var_51] = (compute_4[cse_var_51] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 9)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 512)], 0f32)))
-                  compute_4[cse_var_50] = (compute_4[cse_var_50] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 10)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 512)], 0f32)))
-                  compute_4[cse_var_49] = (compute_4[cse_var_49] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 11)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 512)], 0f32)))
-                  compute_4[cse_var_48] = (compute_4[cse_var_48] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 12)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 512)], 0f32)))
-                  compute_4[cse_var_47] = (compute_4[cse_var_47] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 13)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 512)], 0f32)))
-                  compute_4[cse_var_46] = (compute_4[cse_var_46] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 14)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 512)], 0f32)))
-                  compute_4[cse_var_45] = (compute_4[cse_var_45] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 15)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 512)], 0f32)))
-                  compute_4[cse_var_42] = (compute_4[cse_var_42] + (placeholder_1[((placeholder_3[cse_var_1]*16) + cse_var_38)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 768)], 0f32)))
-                  compute_4[cse_var_41] = (compute_4[cse_var_41] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 1)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 768)], 0f32)))
-                  compute_4[cse_var_40] = (compute_4[cse_var_40] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 2)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 768)], 0f32)))
-                  compute_4[cse_var_39] = (compute_4[cse_var_39] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 3)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 768)], 0f32)))
-                  compute_4[cse_var_98] = (compute_4[cse_var_98] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 4)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 768)], 0f32)))
-                  compute_4[cse_var_97] = (compute_4[cse_var_97] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 5)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 768)], 0f32)))
-                  compute_4[cse_var_96] = (compute_4[cse_var_96] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 6)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 768)], 0f32)))
-                  compute_4[cse_var_95] = (compute_4[cse_var_95] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 7)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 768)], 0f32)))
-                  compute_4[cse_var_94] = (compute_4[cse_var_94] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 8)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 768)], 0f32)))
-                  compute_4[cse_var_93] = (compute_4[cse_var_93] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 9)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 768)], 0f32)))
-                  compute_4[cse_var_92] = (compute_4[cse_var_92] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 10)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 768)], 0f32)))
-                  compute_4[cse_var_91] = (compute_4[cse_var_91] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 11)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 768)], 0f32)))
-                  compute_4[cse_var_90] = (compute_4[cse_var_90] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 12)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 768)], 0f32)))
-                  compute_4[cse_var_89] = (compute_4[cse_var_89] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 13)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 768)], 0f32)))
-                  compute_4[cse_var_87] = (compute_4[cse_var_87] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 14)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 768)], 0f32)))
-                  compute_4[cse_var_86] = (compute_4[cse_var_86] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 15)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 768)], 0f32)))
-                  compute_4[cse_var_68] = (compute_4[cse_var_68] + (placeholder_1[((placeholder_3[cse_var_1]*16) + cse_var_38)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1024)], 0f32)))
-                  compute_4[cse_var_83] = (compute_4[cse_var_83] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 1)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1024)], 0f32)))
-                  compute_4[cse_var_81] = (compute_4[cse_var_81] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 2)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1024)], 0f32)))
-                  compute_4[cse_var_80] = (compute_4[cse_var_80] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 3)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1024)], 0f32)))
-                  compute_4[cse_var_79] = (compute_4[cse_var_79] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 4)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1024)], 0f32)))
-                  compute_4[cse_var_78] = (compute_4[cse_var_78] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 5)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1024)], 0f32)))
-                  compute_4[cse_var_77] = (compute_4[cse_var_77] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 6)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1024)], 0f32)))
-                  compute_4[cse_var_76] = (compute_4[cse_var_76] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 7)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1024)], 0f32)))
-                  compute_4[cse_var_75] = (compute_4[cse_var_75] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 8)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1024)], 0f32)))
-                  compute_4[cse_var_74] = (compute_4[cse_var_74] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 9)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1024)], 0f32)))
-                  compute_4[cse_var_73] = (compute_4[cse_var_73] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 10)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1024)], 0f32)))
-                  compute_4[cse_var_72] = (compute_4[cse_var_72] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 11)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1024)], 0f32)))
-                  compute_4[cse_var_70] = (compute_4[cse_var_70] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 12)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1024)], 0f32)))
-                  compute_4[cse_var_69] = (compute_4[cse_var_69] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 13)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1024)], 0f32)))
-                  compute_4[cse_var_84] = (compute_4[cse_var_84] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 14)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1024)], 0f32)))
-                  compute_4[cse_var_131] = (compute_4[cse_var_131] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 15)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1024)], 0f32)))
-                  compute_4[cse_var_129] = (compute_4[cse_var_129] + (placeholder_1[((placeholder_3[cse_var_1]*16) + cse_var_38)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1280)], 0f32)))
-                  compute_4[cse_var_128] = (compute_4[cse_var_128] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 1)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1280)], 0f32)))
-                  compute_4[cse_var_127] = (compute_4[cse_var_127] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 2)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1280)], 0f32)))
-                  compute_4[cse_var_126] = (compute_4[cse_var_126] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 3)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1280)], 0f32)))
-                  compute_4[cse_var_125] = (compute_4[cse_var_125] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 4)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1280)], 0f32)))
-                  compute_4[cse_var_124] = (compute_4[cse_var_124] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 5)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1280)], 0f32)))
-                  compute_4[cse_var_123] = (compute_4[cse_var_123] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 6)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1280)], 0f32)))
-                  compute_4[cse_var_122] = (compute_4[cse_var_122] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 7)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1280)], 0f32)))
-                  compute_4[cse_var_121] = (compute_4[cse_var_121] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 8)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1280)], 0f32)))
-                  compute_4[cse_var_120] = (compute_4[cse_var_120] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 9)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1280)], 0f32)))
-                  compute_4[cse_var_119] = (compute_4[cse_var_119] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 10)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1280)], 0f32)))
-                  compute_4[cse_var_118] = (compute_4[cse_var_118] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 11)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1280)], 0f32)))
-                  compute_4[cse_var_117] = (compute_4[cse_var_117] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 12)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1280)], 0f32)))
-                  compute_4[cse_var_100] = (compute_4[cse_var_100] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 13)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1280)], 0f32)))
-                  compute_4[cse_var_115] = (compute_4[cse_var_115] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 14)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1280)], 0f32)))
-                  compute_4[cse_var_114] = (compute_4[cse_var_114] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 15)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1280)], 0f32)))
-                  compute_4[cse_var_113] = (compute_4[cse_var_113] + (placeholder_1[((placeholder_3[cse_var_1]*16) + cse_var_38)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1536)], 0f32)))
-                  compute_4[cse_var_112] = (compute_4[cse_var_112] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 1)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1536)], 0f32)))
-                  compute_4[cse_var_111] = (compute_4[cse_var_111] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 2)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1536)], 0f32)))
-                  compute_4[cse_var_110] = (compute_4[cse_var_110] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 3)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1536)], 0f32)))
-                  compute_4[cse_var_109] = (compute_4[cse_var_109] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 4)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1536)], 0f32)))
-                  compute_4[cse_var_108] = (compute_4[cse_var_108] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 5)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1536)], 0f32)))
-                  compute_4[cse_var_107] = (compute_4[cse_var_107] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 6)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1536)], 0f32)))
-                  compute_4[cse_var_106] = (compute_4[cse_var_106] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 7)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1536)], 0f32)))
-                  compute_4[cse_var_104] = (compute_4[cse_var_104] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 8)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1536)], 0f32)))
-                  compute_4[cse_var_103] = (compute_4[cse_var_103] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 9)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1536)], 0f32)))
-                  compute_4[cse_var_102] = (compute_4[cse_var_102] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 10)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1536)], 0f32)))
-                  compute_4[cse_var_101] = (compute_4[cse_var_101] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 11)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1536)], 0f32)))
-                  compute_4[cse_var_52] = (compute_4[cse_var_52] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 12)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1536)], 0f32)))
-                  compute_4[cse_var_35] = (compute_4[cse_var_35] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 13)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1536)], 0f32)))
-                  compute_4[cse_var_34] = (compute_4[cse_var_34] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 14)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1536)], 0f32)))
-                  compute_4[cse_var_33] = (compute_4[cse_var_33] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 15)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1536)], 0f32)))
-                  compute_4[cse_var_32] = (compute_4[cse_var_32] + (placeholder_1[((placeholder_3[cse_var_1]*16) + cse_var_38)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1792)], 0f32)))
-                  compute_4[cse_var_31] = (compute_4[cse_var_31] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 1)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1792)], 0f32)))
-                  compute_4[cse_var_30] = (compute_4[cse_var_30] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 2)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1792)], 0f32)))
-                  compute_4[cse_var_29] = (compute_4[cse_var_29] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 3)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1792)], 0f32)))
-                  compute_4[cse_var_28] = (compute_4[cse_var_28] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 4)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1792)], 0f32)))
-                  compute_4[cse_var_27] = (compute_4[cse_var_27] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 5)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1792)], 0f32)))
-                  compute_4[cse_var_26] = (compute_4[cse_var_26] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 6)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1792)], 0f32)))
-                  compute_4[cse_var_25] = (compute_4[cse_var_25] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 7)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1792)], 0f32)))
-                  compute_4[cse_var_24] = (compute_4[cse_var_24] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 8)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1792)], 0f32)))
-                  compute_4[cse_var_23] = (compute_4[cse_var_23] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 9)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1792)], 0f32)))
-                  compute_4[cse_var_22] = (compute_4[cse_var_22] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 10)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1792)], 0f32)))
-                  compute_4[cse_var_21] = (compute_4[cse_var_21] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 11)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1792)], 0f32)))
-                  compute_4[cse_var_12] = (compute_4[cse_var_12] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 12)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1792)], 0f32)))
-                  compute_4[cse_var_4] = (compute_4[cse_var_4] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 13)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1792)], 0f32)))
-                  compute_4[cse_var_5] = (compute_4[cse_var_5] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 14)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1792)], 0f32)))
-                  compute_4[cse_var_6] = (compute_4[cse_var_6] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 15)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1792)], 0f32)))
+                  compute_4: Buffer(compute_3, float32, [2048], [])[cse_var_1] = 0f32
+                  compute_4[(cse_var_1 + 1)] = 0f32
+                  compute_4[(cse_var_1 + 2)] = 0f32
+                  compute_4[(cse_var_1 + 3)] = 0f32
+                  compute_4[(cse_var_1 + 4)] = 0f32
+                  compute_4[(cse_var_1 + 5)] = 0f32
+                  compute_4[(cse_var_1 + 6)] = 0f32
+                  compute_4[(cse_var_1 + 7)] = 0f32
+                  compute_4[(cse_var_1 + 8)] = 0f32
+                  compute_4[(cse_var_1 + 9)] = 0f32
+                  compute_4[(cse_var_1 + 10)] = 0f32
+                  compute_4[(cse_var_1 + 11)] = 0f32
+                  compute_4[(cse_var_1 + 12)] = 0f32
+                  compute_4[(cse_var_1 + 13)] = 0f32
+                  compute_4[(cse_var_1 + 14)] = 0f32
+                  compute_4[(cse_var_1 + 15)] = 0f32
+                }
+              }
+              for (elem_idx: int32, 0, let cse_var_2: int32 = ((floormod(i0.outer.i1.outer.fused, 16)*2) + nb_j.inner) in (placeholder_3[(cse_var_2 + 1)] - placeholder_3[cse_var_2])) {
+                for (i.inner: int32, 0, 16) {
+                  let cse_var_21: int32 = (elem_idx*16)
+                  let cse_var_20: int32 = ((floormod(i0.outer.i1.outer.fused, 16)*2) + nb_j.inner)
+                  let cse_var_19: int32 = (((i.outer.inner*512) + (i.inner*32)) + (nb_j.inner*16))
+                  let cse_var_18: int32 = (cse_var_19 + 1)
+                  let cse_var_17: int32 = (cse_var_19 + 11)
+                  let cse_var_16: int32 = (cse_var_19 + 12)
+                  let cse_var_15: int32 = (cse_var_19 + 13)
+                  let cse_var_14: int32 = (cse_var_19 + 14)
+                  let cse_var_13: int32 = (cse_var_19 + 15)
+                  let cse_var_12: int32 = (cse_var_19 + 2)
+                  let cse_var_11: int32 = (cse_var_19 + 3)
+                  let cse_var_10: int32 = (cse_var_19 + 4)
+                  let cse_var_9: int32 = (cse_var_19 + 5)
+                  let cse_var_8: int32 = (cse_var_19 + 6)
+                  let cse_var_7: int32 = (cse_var_19 + 7)
+                  let cse_var_6: int32 = (cse_var_19 + 8)
+                  let cse_var_5: int32 = (cse_var_19 + 9)
+                  let cse_var_4: int32 = (((floordiv(i0.outer.i1.outer.fused, 16)*16384) + (i.outer.inner*4096)) + (i.inner*256))
+                  let cse_var_3: int32 = (cse_var_19 + 10)
+                   {
+                    compute_4[cse_var_19] = (compute_4[cse_var_19] + (placeholder_1[((placeholder_3[cse_var_20]*16) + cse_var_21)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+                    compute_4[cse_var_18] = (compute_4[cse_var_18] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 1)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+                    compute_4[cse_var_12] = (compute_4[cse_var_12] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 2)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+                    compute_4[cse_var_11] = (compute_4[cse_var_11] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 3)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+                    compute_4[cse_var_10] = (compute_4[cse_var_10] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 4)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+                    compute_4[cse_var_9] = (compute_4[cse_var_9] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 5)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+                    compute_4[cse_var_8] = (compute_4[cse_var_8] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 6)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+                    compute_4[cse_var_7] = (compute_4[cse_var_7] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 7)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+                    compute_4[cse_var_6] = (compute_4[cse_var_6] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 8)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+                    compute_4[cse_var_5] = (compute_4[cse_var_5] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 9)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+                    compute_4[cse_var_3] = (compute_4[cse_var_3] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 10)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+                    compute_4[cse_var_17] = (compute_4[cse_var_17] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 11)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+                    compute_4[cse_var_16] = (compute_4[cse_var_16] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 12)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+                    compute_4[cse_var_15] = (compute_4[cse_var_15] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 13)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+                    compute_4[cse_var_14] = (compute_4[cse_var_14] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 14)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+                    compute_4[cse_var_13] = (compute_4[cse_var_13] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 15)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+                  }
                 }
               }
             }
           }
-          for (i0.inner: int32, 0, 8) {
-            let cse_var_132: int32 = (((i0.outer*4096) + (i0.inner*512)) + (i1.outer*32))
-            compute[ramp(cse_var_132, 1, 32)] = max((compute_4[ramp((i0.inner*32), 1, 32)] + placeholder_4[ramp(cse_var_132, 1, 32)]), broadcast(0f32, 32))
+          for (i0.inner: int32, 0, 64) {
+            let cse_var_22: int32 = (((floordiv(i0.outer.i1.outer.fused, 16)*32768) + (i0.inner*512)) + (floormod(i0.outer.i1.outer.fused, 16)*32))
+            compute[ramp(cse_var_22, 1, 32)] = max((compute_4[ramp((i0.inner*32), 1, 32)] + placeholder_4[ramp(cse_var_22, 1, 32)]), broadcast(0f32, 32))
           }
         }
       }
@@ -816,7 +486,7 @@ We build the binary and check its correctness and performance.
 
  .. code-block:: none
 
-    Execution time of this operator: 2.706 ms
+    Execution time of this operator: 1.721 ms
 
 
 
diff --git a/docs/_sources/how_to/tune_with_autotvm/sg_execution_times.rst.txt b/docs/_sources/how_to/tune_with_autotvm/sg_execution_times.rst.txt
index 4f5f0a1a9..16d6f9f1d 100644
--- a/docs/_sources/how_to/tune_with_autotvm/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/tune_with_autotvm/sg_execution_times.rst.txt
@@ -5,10 +5,10 @@
 
 Computation times
 =================
-**00:43.955** total execution time for **how_to_tune_with_autotvm** files:
+**00:44.244** total execution time for **how_to_tune_with_autotvm** files:
 
-- **00:43.085**: :ref:`sphx_glr_how_to_tune_with_autotvm_tune_conv2d_cuda.py` (``tune_conv2d_cuda.py``)
-- **00:00.228**: :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_x86.py` (``tune_relay_x86.py``)
-- **00:00.216**: :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_arm.py` (``tune_relay_arm.py``)
-- **00:00.213**: :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_mobile_gpu.py` (``tune_relay_mobile_gpu.py``)
-- **00:00.213**: :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_cuda.py` (``tune_relay_cuda.py``)
+- **00:43.367**: :ref:`sphx_glr_how_to_tune_with_autotvm_tune_conv2d_cuda.py` (``tune_conv2d_cuda.py``)
+- **00:00.232**: :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_x86.py` (``tune_relay_x86.py``)
+- **00:00.218**: :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_cuda.py` (``tune_relay_cuda.py``)
+- **00:00.214**: :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_mobile_gpu.py` (``tune_relay_mobile_gpu.py``)
+- **00:00.214**: :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_arm.py` (``tune_relay_arm.py``)
diff --git a/docs/_sources/how_to/tune_with_autotvm/tune_conv2d_cuda.rst.txt b/docs/_sources/how_to/tune_with_autotvm/tune_conv2d_cuda.rst.txt
index f28f2ec32..359eb03f5 100644
--- a/docs/_sources/how_to/tune_with_autotvm/tune_conv2d_cuda.rst.txt
+++ b/docs/_sources/how_to/tune_with_autotvm/tune_conv2d_cuda.rst.txt
@@ -859,8 +859,8 @@ for this template
       File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 854, in verify_pass
         raise InstantiationError("Skipped because of invalid gpu kernel")
     tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel        [('tile_f', [-1, 4, 4, 32]), ('tile_y', [-1, 1, 1, 7]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 1, 128]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 512), ('unroll_explicit', 0)],None,2885496
-    No: 6   GFLOPS: 103.76/103.76   result: MeasureResult(costs=(0.0022312065416666667,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.5933806896209717, timestamp=1650065132.1649663)      [('tile_f', [-1, 1, 1, 1]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 4, 4]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,3754080
-    No: 7   GFLOPS: 0.00/103.76     result: Traceback (most recent call last):
+    No: 6   GFLOPS: 42.31/42.31     result: MeasureResult(costs=(0.005471151894736842,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.6274833679199219, timestamp=1650168007.1835592)       [('tile_f', [-1, 1, 1, 1]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 4, 4]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,3754080
+    No: 7   GFLOPS: 0.00/42.31      result: Traceback (most recent call last):
       File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 571, in __call__
         func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
       File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 523, in _build_func_common
@@ -983,7 +983,7 @@ for this template
       File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 854, in verify_pass
         raise InstantiationError("Skipped because of invalid gpu kernel")
     tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel        [('tile_f', [-1, 1, 16, 32]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 256, 1]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 0), ('unroll_explicit', 1)],None,6225319
-    No: 8   GFLOPS: 0.00/103.76     result: Traceback (most recent call last):
+    No: 8   GFLOPS: 0.00/42.31      result: Traceback (most recent call last):
       File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 571, in __call__
         func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
       File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 523, in _build_func_common
@@ -1106,7 +1106,7 @@ for this template
       File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 854, in verify_pass
         raise InstantiationError("Skipped because of invalid gpu kernel")
     tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel        [('tile_f', [-1, 2, 1, 32]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 1, 1]), ('tile_rc', [-1, 8, 64]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 0), ('unroll_explicit', 0)],None,943546
-    No: 9   GFLOPS: 0.00/103.76     result: Traceback (most recent call last):
+    No: 9   GFLOPS: 0.00/42.31      result: Traceback (most recent call last):
       File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 571, in __call__
         func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
       File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 523, in _build_func_common
@@ -1229,7 +1229,7 @@ for this template
       File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 854, in verify_pass
         raise InstantiationError("Skipped because of invalid gpu kernel")
     tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel        [('tile_f', [-1, 4, 16, 4]), ('tile_y', [-1, 1, 1, 7]), ('tile_x', [-1, 1, 1, 7]), ('tile_rc', [-1, 16, 32]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 512), ('unroll_explicit', 0)],None,2868708
-    No: 10  GFLOPS: 0.00/103.76     result: Traceback (most recent call last):
+    No: 10  GFLOPS: 0.00/42.31      result: Traceback (most recent call last):
       File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 142, in build
         res = future.result()
       File "/usr/lib/python3.7/concurrent/futures/_base.py", line 435, in result
@@ -1247,7 +1247,7 @@ for this template
     TimeoutError
 
             [('tile_f', [-1, 32, 2, 4]), ('tile_y', [-1, 1, 7, 1]), ('tile_x', [-1, 1, 1, 7]), ('tile_rc', [-1, 4, 2]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,4691833
-    No: 11  GFLOPS: 0.00/103.76     result: Traceback (most recent call last):
+    No: 11  GFLOPS: 0.00/42.31      result: Traceback (most recent call last):
       File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 571, in __call__
         func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
       File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 523, in _build_func_common
@@ -1370,7 +1370,7 @@ for this template
       File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 854, in verify_pass
         raise InstantiationError("Skipped because of invalid gpu kernel")
     tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel        [('tile_f', [-1, 1, 2, 64]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 1, 1]), ('tile_rc', [-1, 4, 4]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 0), ('unroll_explicit', 0)],None,1042124
-    No: 12  GFLOPS: 0.00/103.76     result: Traceback (most recent call last):
+    No: 12  GFLOPS: 0.00/42.31      result: Traceback (most recent call last):
       File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 571, in __call__
         func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
       File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 523, in _build_func_common
@@ -1493,7 +1493,7 @@ for this template
       File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 854, in verify_pass
         raise InstantiationError("Skipped because of invalid gpu kernel")
     tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel        [('tile_f', [-1, 32, 1, 4]), ('tile_y', [-1, 1, 1, 7]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 32, 16]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 1)],None,10013405
-    No: 13  GFLOPS: 0.00/103.76     result: Traceback (most recent call last):
+    No: 13  GFLOPS: 0.00/42.31      result: Traceback (most recent call last):
       File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 571, in __call__
         func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
       File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 523, in _build_func_common
@@ -1616,7 +1616,7 @@ for this template
       File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 854, in verify_pass
         raise InstantiationError("Skipped because of invalid gpu kernel")
     tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel        [('tile_f', [-1, 8, 8, 2]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 4, 32]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 0), ('unroll_explicit', 1)],None,6732082
-    No: 14  GFLOPS: 0.00/103.76     result: Traceback (most recent call last):
+    No: 14  GFLOPS: 0.00/42.31      result: Traceback (most recent call last):
       File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 571, in __call__
         func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
       File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 523, in _build_func_common
@@ -1739,7 +1739,7 @@ for this template
       File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 854, in verify_pass
         raise InstantiationError("Skipped because of invalid gpu kernel")
     tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel        [('tile_f', [-1, 2, 4, 32]), ('tile_y', [-1, 7, 1, 1]), ('tile_x', [-1, 1, 1, 1]), ('tile_rc', [-1, 4, 128]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 512), ('unroll_explicit', 1)],None,7536735
-    No: 15  GFLOPS: 0.00/103.76     result: Traceback (most recent call last):
+    No: 15  GFLOPS: 0.00/42.31      result: Traceback (most recent call last):
       File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 571, in __call__
         func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
       File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 523, in _build_func_common
@@ -1862,7 +1862,7 @@ for this template
       File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 854, in verify_pass
         raise InstantiationError("Skipped because of invalid gpu kernel")
     tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel        [('tile_f', [-1, 2, 1, 4]), ('tile_y', [-1, 1, 1, 7]), ('tile_x', [-1, 1, 1, 7]), ('tile_rc', [-1, 128, 4]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 0), ('unroll_explicit', 0)],None,482121
-    No: 16  GFLOPS: 0.00/103.76     result: Traceback (most recent call last):
+    No: 16  GFLOPS: 0.00/42.31      result: Traceback (most recent call last):
       File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 571, in __call__
         func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
       File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 523, in _build_func_common
@@ -1985,7 +1985,7 @@ for this template
       File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 854, in verify_pass
         raise InstantiationError("Skipped because of invalid gpu kernel")
     tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel        [('tile_f', [-1, 2, 1, 16]), ('tile_y', [-1, 1, 7, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 32, 8]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 512), ('unroll_explicit', 0)],None,2824525
-    No: 17  GFLOPS: 0.00/103.76     result: Traceback (most recent call last):
+    No: 17  GFLOPS: 0.00/42.31      result: Traceback (most recent call last):
       File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 571, in __call__
         func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
       File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 523, in _build_func_common
@@ -2108,7 +2108,7 @@ for this template
       File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 854, in verify_pass
         raise InstantiationError("Skipped because of invalid gpu kernel")
     tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel        [('tile_f', [-1, 64, 1, 1]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 8, 8]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 0)],None,4559286
-    No: 18  GFLOPS: 0.00/103.76     result: Traceback (most recent call last):
+    No: 18  GFLOPS: 0.00/42.31      result: Traceback (most recent call last):
       File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 571, in __call__
         func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
       File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 523, in _build_func_common
@@ -2231,7 +2231,7 @@ for this template
       File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 854, in verify_pass
         raise InstantiationError("Skipped because of invalid gpu kernel")
     tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel        [('tile_f', [-1, 1, 32, 16]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 1, 512]), ('tile_ry', [-1, 3, 1]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 1)],None,9677544
-    No: 19  GFLOPS: 0.00/103.76     result: Traceback (most recent call last):
+    No: 19  GFLOPS: 0.00/42.31      result: Traceback (most recent call last):
       File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 721, in __call__
         yield remote, remote.load_module(os.path.split(build_result.filename)[1])
       File "/workspace/python/tvm/autotvm/measure/measure_methods.py", line 685, in run_through_rpc
@@ -2319,7 +2319,7 @@ for this template
       15: _PyEval_EvalFrameDefault
       14: 0x0000000000537c30
       13: _PyObject_FastCallKeywords
-      12: 0x00007f0bfab37fa2
+      12: 0x00007f5b7c486fa2
       11: _ctypes_callproc
       10: ffi_call
       9: ffi_call_unix64
@@ -2384,7 +2384,7 @@ for this template
       21: _PyFunction_FastCallKeywords
       20: _PyEval_EvalFrameDefault
       19: _PyFunction_FastCall      [('tile_f', [-1, 8, 2, 16]), ('tile_y', [-1, 7, 1, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 1, 1]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 0), ('unroll_explicit', 1)],None,6390073
-    No: 20  GFLOPS: 144.55/144.55   result: MeasureResult(costs=(0.0016015775600000002,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.4185152053833008, timestamp=1650065158.492134)       [('tile_f', [-1, 1, 4, 1]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 4, 1]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 1)],None,9881539
+    No: 20  GFLOPS: 143.76/143.76   result: MeasureResult(costs=(0.0016103786199999997,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.4413950443267822, timestamp=1650168033.6066215)      [('tile_f', [-1, 1, 4, 1]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 4, 1]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 1)],None,9881539
 
 
 
@@ -2437,7 +2437,7 @@ and measure running time.
 
     Best config:
     [('tile_f', [-1, 1, 4, 1]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 4, 1]), ('tile_ry', [-1, 1, 1]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 1)],None,9881539
-    Time cost of this operator: 0.002011
+    Time cost of this operator: 0.002038
 
 
 
diff --git a/docs/_sources/how_to/work_with_microtvm/micro_autotune.rst.txt b/docs/_sources/how_to/work_with_microtvm/micro_autotune.rst.txt
index ccd06cdb0..8a8bc2760 100644
--- a/docs/_sources/how_to/work_with_microtvm/micro_autotune.rst.txt
+++ b/docs/_sources/how_to/work_with_microtvm/micro_autotune.rst.txt
@@ -292,10 +292,10 @@ Timing the untuned program
     ########## Build without Autotuning ##########
     Node Name                                     Ops                                           Time(us)  Time(%)  Shape              Inputs  Outputs  
     ---------                                     ---                                           --------  -------  -----              ------  -------  
-    tvmgen_default_fused_nn_contrib_conv2d_NCHWc  tvmgen_default_fused_nn_contrib_conv2d_NCHWc  313.0     98.744   (1, 2, 10, 10, 3)  2       1        
-    tvmgen_default_fused_layout_transform_1       tvmgen_default_fused_layout_transform_1       3.073     0.969    (1, 6, 10, 10)     1       1        
-    tvmgen_default_fused_layout_transform         tvmgen_default_fused_layout_transform         0.907     0.286    (1, 1, 10, 10, 3)  1       1        
-    Total_time                                    -                                             316.98    -        -                  -       -        
+    tvmgen_default_fused_nn_contrib_conv2d_NCHWc  tvmgen_default_fused_nn_contrib_conv2d_NCHWc  313.4     98.695   (1, 2, 10, 10, 3)  2       1        
+    tvmgen_default_fused_layout_transform_1       tvmgen_default_fused_layout_transform_1       3.153     0.993    (1, 6, 10, 10)     1       1        
+    tvmgen_default_fused_layout_transform         tvmgen_default_fused_layout_transform         0.991     0.312    (1, 1, 10, 10, 3)  1       1        
+    Total_time                                    -                                             317.544   -        -                  -       -        
 
 
 
@@ -357,10 +357,10 @@ Timing the tuned program
     ########## Build with Autotuning ##########
     Node Name                                     Ops                                           Time(us)  Time(%)  Shape              Inputs  Outputs  
     ---------                                     ---                                           --------  -------  -----              ------  -------  
-    tvmgen_default_fused_nn_contrib_conv2d_NCHWc  tvmgen_default_fused_nn_contrib_conv2d_NCHWc  133.7     98.082   (1, 6, 10, 10, 1)  2       1        
-    tvmgen_default_fused_layout_transform_1       tvmgen_default_fused_layout_transform_1       1.702     1.248    (1, 6, 10, 10)     1       1        
-    tvmgen_default_fused_layout_transform         tvmgen_default_fused_layout_transform         0.913     0.67     (1, 1, 10, 10, 3)  1       1        
-    Total_time                                    -                                             136.314   -        -                  -       -        
+    tvmgen_default_fused_nn_contrib_conv2d_NCHWc  tvmgen_default_fused_nn_contrib_conv2d_NCHWc  324.8     98.792   (1, 2, 10, 10, 3)  2       1        
+    tvmgen_default_fused_layout_transform_1       tvmgen_default_fused_layout_transform_1       3.07      0.934    (1, 6, 10, 10)     1       1        
+    tvmgen_default_fused_layout_transform         tvmgen_default_fused_layout_transform         0.901     0.274    (1, 1, 10, 10, 3)  1       1        
+    Total_time                                    -                                             328.771   -        -                  -       -        
 
 
 
diff --git a/docs/_sources/how_to/work_with_microtvm/sg_execution_times.rst.txt b/docs/_sources/how_to/work_with_microtvm/sg_execution_times.rst.txt
index d8594a20a..b9c879082 100644
--- a/docs/_sources/how_to/work_with_microtvm/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/work_with_microtvm/sg_execution_times.rst.txt
@@ -5,10 +5,10 @@
 
 Computation times
 =================
-**00:43.330** total execution time for **how_to_work_with_microtvm** files:
+**00:46.129** total execution time for **how_to_work_with_microtvm** files:
 
-- **00:39.365**: :ref:`sphx_glr_how_to_work_with_microtvm_micro_autotune.py` (``micro_autotune.py``)
-- **00:03.415**: :ref:`sphx_glr_how_to_work_with_microtvm_micro_tflite.py` (``micro_tflite.py``)
-- **00:00.190**: :ref:`sphx_glr_how_to_work_with_microtvm_micro_tvmc.py` (``micro_tvmc.py``)
-- **00:00.184**: :ref:`sphx_glr_how_to_work_with_microtvm_micro_ethosu.py` (``micro_ethosu.py``)
-- **00:00.176**: :ref:`sphx_glr_how_to_work_with_microtvm_micro_reference_vm.py` (``micro_reference_vm.py``)
+- **00:41.923**: :ref:`sphx_glr_how_to_work_with_microtvm_micro_autotune.py` (``micro_autotune.py``)
+- **00:03.605**: :ref:`sphx_glr_how_to_work_with_microtvm_micro_tflite.py` (``micro_tflite.py``)
+- **00:00.201**: :ref:`sphx_glr_how_to_work_with_microtvm_micro_ethosu.py` (``micro_ethosu.py``)
+- **00:00.201**: :ref:`sphx_glr_how_to_work_with_microtvm_micro_reference_vm.py` (``micro_reference_vm.py``)
+- **00:00.199**: :ref:`sphx_glr_how_to_work_with_microtvm_micro_tvmc.py` (``micro_tvmc.py``)
diff --git a/docs/_sources/how_to/work_with_relay/sg_execution_times.rst.txt b/docs/_sources/how_to/work_with_relay/sg_execution_times.rst.txt
index cf2f8d765..011f0ba13 100644
--- a/docs/_sources/how_to/work_with_relay/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/work_with_relay/sg_execution_times.rst.txt
@@ -5,8 +5,8 @@
 
 Computation times
 =================
-**00:09.148** total execution time for **how_to_work_with_relay** files:
+**00:09.486** total execution time for **how_to_work_with_relay** files:
 
-- **00:07.078**: :ref:`sphx_glr_how_to_work_with_relay_using_external_lib.py` (``using_external_lib.py``)
-- **00:01.882**: :ref:`sphx_glr_how_to_work_with_relay_build_gcn.py` (``build_gcn.py``)
-- **00:00.188**: :ref:`sphx_glr_how_to_work_with_relay_using_relay_viz.py` (``using_relay_viz.py``)
+- **00:07.191**: :ref:`sphx_glr_how_to_work_with_relay_using_external_lib.py` (``using_external_lib.py``)
+- **00:02.079**: :ref:`sphx_glr_how_to_work_with_relay_build_gcn.py` (``build_gcn.py``)
+- **00:00.215**: :ref:`sphx_glr_how_to_work_with_relay_using_relay_viz.py` (``using_relay_viz.py``)
diff --git a/docs/_sources/how_to/work_with_schedules/sg_execution_times.rst.txt b/docs/_sources/how_to/work_with_schedules/sg_execution_times.rst.txt
index d7df8dfd7..e73510489 100644
--- a/docs/_sources/how_to/work_with_schedules/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/work_with_schedules/sg_execution_times.rst.txt
@@ -5,13 +5,13 @@
 
 Computation times
 =================
-**00:05.384** total execution time for **how_to_work_with_schedules** files:
+**00:05.609** total execution time for **how_to_work_with_schedules** files:
 
-- **00:02.025**: :ref:`sphx_glr_how_to_work_with_schedules_intrin_math.py` (``intrin_math.py``)
-- **00:01.059**: :ref:`sphx_glr_how_to_work_with_schedules_tensorize.py` (``tensorize.py``)
-- **00:00.701**: :ref:`sphx_glr_how_to_work_with_schedules_reduction.py` (``reduction.py``)
-- **00:00.690**: :ref:`sphx_glr_how_to_work_with_schedules_scan.py` (``scan.py``)
-- **00:00.277**: :ref:`sphx_glr_how_to_work_with_schedules_extern_op.py` (``extern_op.py``)
-- **00:00.231**: :ref:`sphx_glr_how_to_work_with_schedules_schedule_primitives.py` (``schedule_primitives.py``)
-- **00:00.207**: :ref:`sphx_glr_how_to_work_with_schedules_tedd.py` (``tedd.py``)
-- **00:00.194**: :ref:`sphx_glr_how_to_work_with_schedules_tuple_inputs.py` (``tuple_inputs.py``)
+- **00:02.080**: :ref:`sphx_glr_how_to_work_with_schedules_intrin_math.py` (``intrin_math.py``)
+- **00:01.080**: :ref:`sphx_glr_how_to_work_with_schedules_tensorize.py` (``tensorize.py``)
+- **00:00.726**: :ref:`sphx_glr_how_to_work_with_schedules_reduction.py` (``reduction.py``)
+- **00:00.715**: :ref:`sphx_glr_how_to_work_with_schedules_scan.py` (``scan.py``)
+- **00:00.311**: :ref:`sphx_glr_how_to_work_with_schedules_extern_op.py` (``extern_op.py``)
+- **00:00.239**: :ref:`sphx_glr_how_to_work_with_schedules_schedule_primitives.py` (``schedule_primitives.py``)
+- **00:00.235**: :ref:`sphx_glr_how_to_work_with_schedules_tedd.py` (``tedd.py``)
+- **00:00.223**: :ref:`sphx_glr_how_to_work_with_schedules_tuple_inputs.py` (``tuple_inputs.py``)
diff --git a/docs/_sources/how_to/work_with_schedules/tensorize.rst.txt b/docs/_sources/how_to/work_with_schedules/tensorize.rst.txt
index 2f539e4b1..bbe43b210 100644
--- a/docs/_sources/how_to/work_with_schedules/tensorize.rst.txt
+++ b/docs/_sources/how_to/work_with_schedules/tensorize.rst.txt
@@ -314,7 +314,7 @@ The importing needs to happen before the tensorized GEMV being executed.
                  B: Buffer(B_2: Pointer(float32), float32, [32768], []),
                  C: Buffer(C_2: Pointer(float32), float32, [524288], [])}
       buffer_map = {A_1: A, B_1: B, C_1: C} {
-      attr [IterVar(i: int32, (nullptr), "DataPar", "")] "pragma_import_llvm" = "; ModuleID = '/tmp/tmp5asqara1/input0.cc'\nsource_filename = \"/tmp/tmp5asqara1/input0.cc\"\ntarget datalayout = \"e-m:e-i64:64-f80:128-n8:16:32:64-S128\"\ntarget triple = \"x86_64-pc-linux-gnu\"\n\n; Function Attrs: noinline nounwind optnone uwtable\ndefine dso_local i32 @gemv_update(float*, float*, float*, i32, i32, i32) #0 {\n  %7 = alloca float*, align 8\n  %8 = alloca float*, align 8\n  %9 = alloca floa [...]
+      attr [IterVar(i: int32, (nullptr), "DataPar", "")] "pragma_import_llvm" = "; ModuleID = '/tmp/tmpzd4fx58c/input0.cc'\nsource_filename = \"/tmp/tmpzd4fx58c/input0.cc\"\ntarget datalayout = \"e-m:e-i64:64-f80:128-n8:16:32:64-S128\"\ntarget triple = \"x86_64-pc-linux-gnu\"\n\n; Function Attrs: noinline nounwind optnone uwtable\ndefine dso_local i32 @gemv_update(float*, float*, float*, i32, i32, i32) #0 {\n  %7 = alloca float*, align 8\n  %8 = alloca float*, align 8\n  %9 = alloca floa [...]
       for (i, 0, 1024) {
         for (j.outer: int32, 0, 32) {
           @tir.call_extern("gemv_update", @tir.tvm_access_ptr(@tir.type_annotation(, dtype=float32), C_2, ((i*512) + (j.outer*16)), 16, 2, dtype=handle), @tir.tvm_access_ptr(@tir.type_annotation(, dtype=float32), A_2, (i*64), 64, 1, dtype=handle), @tir.tvm_access_ptr(@tir.type_annotation(, dtype=float32), B_2, (j.outer*1024), 1024, 1, dtype=handle), 16, 64, 64, dtype=int32)
diff --git a/docs/_sources/topic/vta/tutorials/autotvm/sg_execution_times.rst.txt b/docs/_sources/topic/vta/tutorials/autotvm/sg_execution_times.rst.txt
index fb29ef755..d404bd451 100644
--- a/docs/_sources/topic/vta/tutorials/autotvm/sg_execution_times.rst.txt
+++ b/docs/_sources/topic/vta/tutorials/autotvm/sg_execution_times.rst.txt
@@ -5,7 +5,7 @@
 
 Computation times
 =================
-**00:20.420** total execution time for **topic_vta_tutorials_autotvm** files:
+**00:21.794** total execution time for **topic_vta_tutorials_autotvm** files:
 
-- **00:20.224**: :ref:`sphx_glr_topic_vta_tutorials_autotvm_tune_relay_vta.py` (``tune_relay_vta.py``)
-- **00:00.196**: :ref:`sphx_glr_topic_vta_tutorials_autotvm_tune_alu_vta.py` (``tune_alu_vta.py``)
+- **00:21.586**: :ref:`sphx_glr_topic_vta_tutorials_autotvm_tune_relay_vta.py` (``tune_relay_vta.py``)
+- **00:00.208**: :ref:`sphx_glr_topic_vta_tutorials_autotvm_tune_alu_vta.py` (``tune_alu_vta.py``)
diff --git a/docs/_sources/topic/vta/tutorials/frontend/deploy_classification.rst.txt b/docs/_sources/topic/vta/tutorials/frontend/deploy_classification.rst.txt
index 6dcc4582a..34b66808b 100644
--- a/docs/_sources/topic/vta/tutorials/frontend/deploy_classification.rst.txt
+++ b/docs/_sources/topic/vta/tutorials/frontend/deploy_classification.rst.txt
@@ -265,7 +265,7 @@ The compilation steps are:
       DeprecationWarning,
     /workspace/vta/tutorials/frontend/deploy_classification.py:213: DeprecationWarning: legacy graph executor behavior of producing json / lib / params will be removed in the next release. Please see documents of tvm.contrib.graph_executor.GraphModule for the  new recommended usage.
       relay_prog, target=tvm.target.Target(target, host=env.target_host), params=params
-    resnet18_v1 inference graph built in 21.20s!
+    resnet18_v1 inference graph built in 22.93s!
 
 
 
diff --git a/docs/_sources/topic/vta/tutorials/frontend/deploy_detection.rst.txt b/docs/_sources/topic/vta/tutorials/frontend/deploy_detection.rst.txt
index 57b07b313..7b0974fbf 100644
--- a/docs/_sources/topic/vta/tutorials/frontend/deploy_detection.rst.txt
+++ b/docs/_sources/topic/vta/tutorials/frontend/deploy_detection.rst.txt
@@ -301,7 +301,7 @@ The compilation steps are:
 
     /workspace/python/tvm/relay/build_module.py:439: DeprecationWarning: Please use input parameter mod (tvm.IRModule) instead of deprecated parameter mod (tvm.relay.function.Function)
       DeprecationWarning,
-    yolov3-tiny inference graph built in 14.79s!
+    yolov3-tiny inference graph built in 15.89s!
 
 
 
diff --git a/docs/_sources/topic/vta/tutorials/frontend/sg_execution_times.rst.txt b/docs/_sources/topic/vta/tutorials/frontend/sg_execution_times.rst.txt
index 961a680b5..c74409924 100644
--- a/docs/_sources/topic/vta/tutorials/frontend/sg_execution_times.rst.txt
+++ b/docs/_sources/topic/vta/tutorials/frontend/sg_execution_times.rst.txt
@@ -5,7 +5,7 @@
 
 Computation times
 =================
-**01:27.783** total execution time for **topic_vta_tutorials_frontend** files:
+**01:31.294** total execution time for **topic_vta_tutorials_frontend** files:
 
-- **00:46.643**: :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_detection.py` (``deploy_detection.py``)
-- **00:41.140**: :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_classification.py` (``deploy_classification.py``)
+- **00:48.077**: :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_detection.py` (``deploy_detection.py``)
+- **00:43.217**: :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_classification.py` (``deploy_classification.py``)
diff --git a/docs/_sources/topic/vta/tutorials/optimize/sg_execution_times.rst.txt b/docs/_sources/topic/vta/tutorials/optimize/sg_execution_times.rst.txt
index 0ac418b84..ef7bc148f 100644
--- a/docs/_sources/topic/vta/tutorials/optimize/sg_execution_times.rst.txt
+++ b/docs/_sources/topic/vta/tutorials/optimize/sg_execution_times.rst.txt
@@ -5,7 +5,7 @@
 
 Computation times
 =================
-**00:03.500** total execution time for **topic_vta_tutorials_optimize** files:
+**00:03.518** total execution time for **topic_vta_tutorials_optimize** files:
 
-- **00:02.969**: :ref:`sphx_glr_topic_vta_tutorials_optimize_convolution_opt.py` (``convolution_opt.py``)
-- **00:00.531**: :ref:`sphx_glr_topic_vta_tutorials_optimize_matrix_multiply_opt.py` (``matrix_multiply_opt.py``)
+- **00:02.973**: :ref:`sphx_glr_topic_vta_tutorials_optimize_convolution_opt.py` (``convolution_opt.py``)
+- **00:00.546**: :ref:`sphx_glr_topic_vta_tutorials_optimize_matrix_multiply_opt.py` (``matrix_multiply_opt.py``)
diff --git a/docs/_sources/topic/vta/tutorials/sg_execution_times.rst.txt b/docs/_sources/topic/vta/tutorials/sg_execution_times.rst.txt
index 2e18a3e4e..faaef9cab 100644
--- a/docs/_sources/topic/vta/tutorials/sg_execution_times.rst.txt
+++ b/docs/_sources/topic/vta/tutorials/sg_execution_times.rst.txt
@@ -5,7 +5,7 @@
 
 Computation times
 =================
-**00:00.927** total execution time for **topic_vta_tutorials** files:
+**00:00.985** total execution time for **topic_vta_tutorials** files:
 
-- **00:00.471**: :ref:`sphx_glr_topic_vta_tutorials_matrix_multiply.py` (``matrix_multiply.py``)
-- **00:00.456**: :ref:`sphx_glr_topic_vta_tutorials_vta_get_started.py` (``vta_get_started.py``)
+- **00:00.500**: :ref:`sphx_glr_topic_vta_tutorials_matrix_multiply.py` (``matrix_multiply.py``)
+- **00:00.485**: :ref:`sphx_glr_topic_vta_tutorials_vta_get_started.py` (``vta_get_started.py``)
diff --git a/docs/_sources/tutorial/auto_scheduler_matmul_x86.rst.txt b/docs/_sources/tutorial/auto_scheduler_matmul_x86.rst.txt
index a34c5e975..0ae2ad580 100644
--- a/docs/_sources/tutorial/auto_scheduler_matmul_x86.rst.txt
+++ b/docs/_sources/tutorial/auto_scheduler_matmul_x86.rst.txt
@@ -184,7 +184,7 @@ trials, we can load the best schedule from the log file and apply it.
 
  .. code-block:: none
 
-
+    *E
 
 
 
@@ -305,7 +305,7 @@ We build the binary and check its correctness and performance.
 
  .. code-block:: none
 
-    Execution time of this operator: 93.274 ms
+    Execution time of this operator: 94.156 ms
 
 
 
@@ -414,6 +414,11 @@ Expression (TE) language that demonstrates how TVM can optimize computational
 operations.
 
 
+.. rst-class:: sphx-glr-timing
+
+   **Total running time of the script:** ( 1 minutes  11.447 seconds)
+
+
 .. _sphx_glr_download_tutorial_auto_scheduler_matmul_x86.py:
 
 
diff --git a/docs/_sources/tutorial/autotvm_relay_x86.rst.txt b/docs/_sources/tutorial/autotvm_relay_x86.rst.txt
index cf84a334d..9f1c9a5aa 100644
--- a/docs/_sources/tutorial/autotvm_relay_x86.rst.txt
+++ b/docs/_sources/tutorial/autotvm_relay_x86.rst.txt
@@ -268,7 +268,7 @@ standard deviation.
 
  .. code-block:: none
 
-    {'mean': 492.5690862900001, 'median': 492.4631355000031, 'std': 1.3102262374601348}
+    {'mean': 501.5567130400007, 'median': 501.14271980000353, 'std': 1.2693202754407098}
 
 
 
@@ -482,31 +482,31 @@ the tuning data to.
 
  .. code-block:: none
 
-
    [Task  1/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/10) | 0.00 s
    [Task  1/25]  Current/Best:   10.28/  10.28 GFLOPS | Progress: (4/10) | 6.02 s
    [Task  1/25]  Current/Best:    6.49/  23.64 GFLOPS | Progress: (8/10) | 8.43 s
    [Task  1/25]  Current/Best:    4.69/  23.73 GFLOPS | Progress: (10/10) | 10.78 s Done.
-
    [Task  2/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/10) | 0.00 s
    [Task  2/25]  Current/Best:    8.29/  14.64 GFLOPS | Progress: (4/10) | 2.48 s
    [Task  2/25]  Current/Best:   16.06/  18.47 GFLOPS | Progress: (8/10) | 5.16 s
    [Task  2/25]  Current/Best:   15.88/  18.47 GFLOPS | Progress: (10/10) | 5.78 s Done.
-
    [Task  3/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/10) | 0.00 s
    [Task  3/25]  Current/Best:   10.82/  10.82 GFLOPS | Progress: (4/10) | 3.50 s
    [Task  3/25]  Current/Best:   20.96/  23.00 GFLOPS | Progress: (8/10) | 5.06 s
    [Task  3/25]  Current/Best:   16.66/  23.00 GFLOPS | Progress: (10/10) | 5.80 s Done.
-
    [Task  4/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/10) | 0.00 s
    [Task  4/25]  Current/Best:    7.86/  13.29 GFLOPS | Progress: (4/10) | 3.30 s
    [Task  4/25]  Current/Best:   13.72/  16.33 GFLOPS | Progress: (8/10) | 4.98 s
    [Task  4/25]  Current/Best:   20.22/  20.22 GFLOPS | Progress: (10/10) | 6.12 s Done.
-
    [Task  5/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/10) | 0.00 s
    [Task  5/25]  Current/Best:   12.54/  16.00 GFLOPS | Progress: (4/10) | 2.67 s
    [Task  5/25]  Current/Best:    5.44/  17.62 GFLOPS | Progress: (8/10) | 4.55 s
    [Task  5/25]  Current/Best:   17.35/  17.62 GFLOPS | Progress: (10/10) | 5.43 s Done.
-
    [Task  6/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/10) | 0.00 s
    [Task  6/25]  Current/Best:   19.37/  19.37 GFLOPS | Progress: (4/10) | 2.92 s
    [Task  6/25]  Current/Best:   10.08/  19.37 GFLOPS | Progress: (8/10) | 4.92 s
    [Task  6/25]  Current/Best:    9.78/  19.37 GFLOPS | Progress: (10/10) | 6.24 s Done.
-
    [Task  7/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/10) | 0.00 s
    [Task  7/25]  Current/Best:   15.97/  16.63 GFLOPS | Progress: (4/10) | 2.73 s
    [Task  7/25]  Current/Best:   17.82/  17.82 GFLOPS | Progress: (8/10) | 4.52 s
    [Task  7/25]  Current/Best:   14.28/  19.29 GFLOPS | Progress: (10/10) | 5.29 s Done.
-
    [Task  8/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/10) | 0.00 s
    [Task  8/25]  Current/Best:   12.66/  12.73 GFLOPS | Progress: (4/10) | 3.66 s
    [Task  8/25]  Current/Best:   10.49/  15.92 GFLOPS | Progress: (8/10) | 11.18 s
    [Task  8/25]  Current/Best:   12.64/  15.92 GFLOPS | Progress: (10/10) | 12.10 s Done.
-
    [Task  9/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/10) | 0.00 s
    [Task  9/25]  Current/Best:    6.93/  20.88 GFLOPS | Progress: (4/10) | 5.73 s
    [Task  9/25]  Current/Best:    9.11/  20.88 GFLOPS | Progress: (8/10) | 16.96 s
    [Task  9/25]  Current/Best:   15.31/  20.88 GFLOPS | Progress: (10/10) | 17.60 s
    [Task 10/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/10) | 0.00 s
    [Task 10/25]  Current/Best:   12.11/  12.11 GFLOPS | Progress: (4/10) | 2.95 s
    [Task 10/25]  Current/Best:   14.79/  14.87 GFLOPS | Progress: (8/10) | 5.34 s
    [Task 10/25]  Current/Best:   13.11/  14.87 GFLOPS | Progress: (10/10) | 6.37 s Done.
-
    [Task 11/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/10) | 0.00 s
    [Task 11/25]  Current/Best:   14.40/  20.34 GFLOPS | Progress: (4/10) | 3.58 s
    [Task 11/25]  Current/Best:   18.13/  20.67 GFLOPS | Progress: (8/10) | 5.20 s
    [Task 11/25]  Current/Best:   20.56/  20.67 GFLOPS | Progress: (10/10) | 6.31 s Done.
-
    [Task 12/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/10) | 0.00 s
    [Task 12/25]  Current/Best:   15.05/  18.33 GFLOPS | Progress: (4/10) | 2.65 s
    [Task 12/25]  Current/Best:   10.02/  20.13 GFLOPS | Progress: (8/10) | 5.26 s
    [Task 12/25]  Current/Best:   17.95/  20.13 GFLOPS | Progress: (10/10) | 6.60 s Done.
-
    [Task 13/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/10) | 0.00 s
    [Task 13/25]  Current/Best:   10.66/  18.87 GFLOPS | Progress: (4/10) | 3.95 s
    [Task 13/25]  Current/Best:   15.68/  18.87 GFLOPS | Progress: (8/10) | 7.03 s
    [Task 13/25]  Current/Best:   15.29/  18.87 GFLOPS | Progress: (10/10) | 7.98 s Done.
-
    [Task 14/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/10) | 0.00 s
    [Task 14/25]  Current/Best:   14.55/  22.67 GFLOPS | Progress: (4/10) | 2.59 s
    [Task 14/25]  Current/Best:   12.84/  22.67 GFLOPS | Progress: (8/10) | 4.76 s
    [Task 14/25]  Current/Best:   11.20/  22.67 GFLOPS | Progress: (10/10) | 9.04 s Done.
-
    [Task 15/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/10) | 0.00 s
    [Task 15/25]  Current/Best:    3.13/  19.24 GFLOPS | Progress: (4/10) | 2.81 s
    [Task 15/25]  Current/Best:    7.10/  20.90 GFLOPS | Progress: (8/10) | 4.53 s
    [Task 15/25]  Current/Best:   14.32/  20.90 GFLOPS | Progress: (10/10) | 5.16 s
    [Task 16/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/10) | 0.00 s Done.
+
    [Task  1/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/10) | 0.00 s
    [Task  1/25]  Current/Best:   23.71/  23.71 GFLOPS | Progress: (4/10) | 5.94 s
    [Task  1/25]  Current/Best:   13.13/  23.71 GFLOPS | Progress: (8/10) | 11.27 s
    [Task  1/25]  Current/Best:    9.69/  23.71 GFLOPS | Progress: (10/10) | 13.13 s Done.
+
    [Task  2/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/10) | 0.00 s
    [Task  2/25]  Current/Best:   18.80/  18.80 GFLOPS | Progress: (4/10) | 2.77 s
    [Task  2/25]  Current/Best:   14.78/  18.80 GFLOPS | Progress: (8/10) | 4.25 s
    [Task  2/25]  Current/Best:    9.32/  18.80 GFLOPS | Progress: (10/10) | 6.25 s Done.
+
    [Task  3/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/10) | 0.00 s
    [Task  3/25]  Current/Best:   12.42/  24.03 GFLOPS | Progress: (4/10) | 2.50 s
    [Task  3/25]  Current/Best:   11.36/  24.03 GFLOPS | Progress: (8/10) | 4.23 s
    [Task  3/25]  Current/Best:    8.89/  24.03 GFLOPS | Progress: (10/10) | 5.58 s Done.
+
    [Task  4/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/10) | 0.00 s
    [Task  4/25]  Current/Best:   11.54/  17.92 GFLOPS | Progress: (4/10) | 3.77 s
    [Task  4/25]  Current/Best:    5.74/  18.77 GFLOPS | Progress: (8/10) | 5.32 s
    [Task  4/25]  Current/Best:   20.80/  20.80 GFLOPS | Progress: (10/10) | 5.88 s Done.
+
    [Task  5/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/10) | 0.00 s
    [Task  5/25]  Current/Best:    4.06/  20.42 GFLOPS | Progress: (4/10) | 3.41 s
    [Task  5/25]  Current/Best:   18.43/  20.42 GFLOPS | Progress: (8/10) | 5.02 s
    [Task  5/25]  Current/Best:   13.22/  20.42 GFLOPS | Progress: (10/10) | 6.22 s Done.
+
    [Task  6/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/10) | 0.00 s
    [Task  6/25]  Current/Best:   19.51/  19.51 GFLOPS | Progress: (4/10) | 3.21 s
    [Task  6/25]  Current/Best:    8.57/  19.51 GFLOPS | Progress: (8/10) | 5.04 s
    [Task  6/25]  Current/Best:   10.05/  19.51 GFLOPS | Progress: (10/10) | 7.24 s Done.
+
    [Task  7/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/10) | 0.00 s
    [Task  7/25]  Current/Best:    1.59/  17.84 GFLOPS | Progress: (4/10) | 5.29 s
    [Task  7/25]  Current/Best:   15.33/  17.84 GFLOPS | Progress: (8/10) | 8.43 s
    [Task  7/25]  Current/Best:   23.06/  23.06 GFLOPS | Progress: (10/10) | 9.15 s Done.
+
    [Task  8/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/10) | 0.00 s
    [Task  8/25]  Current/Best:   10.99/  15.29 GFLOPS | Progress: (4/10) | 3.80 s
    [Task  8/25]  Current/Best:   10.80/  18.04 GFLOPS | Progress: (8/10) | 6.61 s
    [Task  8/25]  Current/Best:   13.55/  18.04 GFLOPS | Progress: (10/10) | 8.08 s Done.
+
    [Task  9/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/10) | 0.00 s
    [Task  9/25]  Current/Best:    4.76/  16.31 GFLOPS | Progress: (4/10) | 2.87 s
    [Task  9/25]  Current/Best:   16.50/  16.50 GFLOPS | Progress: (8/10) | 6.06 s
    [Task  9/25]  Current/Best:   12.54/  16.50 GFLOPS | Progress: (10/10) | 6.62 s Done.
+
    [Task 10/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/10) | 0.00 s
    [Task 10/25]  Current/Best:    8.55/  16.74 GFLOPS | Progress: (4/10) | 3.41 s
    [Task 10/25]  Current/Best:   16.47/  16.74 GFLOPS | Progress: (8/10) | 5.79 s
    [Task 10/25]  Current/Best:   15.28/  16.74 GFLOPS | Progress: (10/10) | 6.57 s Done.
+
    [Task 11/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/10) | 0.00 s
    [Task 11/25]  Current/Best:   14.63/  23.54 GFLOPS | Progress: (4/10) | 2.77 s
    [Task 11/25]  Current/Best:   21.30/  23.54 GFLOPS | Progress: (8/10) | 5.07 s
    [Task 11/25]  Current/Best:   11.13/  23.54 GFLOPS | Progress: (10/10) | 6.14 s Done.
+
    [Task 12/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/10) | 0.00 s
    [Task 12/25]  Current/Best:    6.73/  18.65 GFLOPS | Progress: (4/10) | 3.75 s
    [Task 12/25]  Current/Best:   12.49/  18.99 GFLOPS | Progress: (8/10) | 5.60 s
    [Task 12/25]  Current/Best:   13.81/  18.99 GFLOPS | Progress: (10/10) | 7.43 s Done.
+
    [Task 13/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/10) | 0.00 s
    [Task 13/25]  Current/Best:    8.87/  20.79 GFLOPS | Progress: (4/10) | 4.84 s
    [Task 13/25]  Current/Best:   22.39/  22.39 GFLOPS | Progress: (8/10) | 8.05 s
    [Task 13/25]  Current/Best:    3.10/  22.39 GFLOPS | Progress: (10/10) | 10.04 s Done.
+
    [Task 14/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/10) | 0.00 s
    [Task 14/25]  Current/Best:   10.93/  11.60 GFLOPS | Progress: (4/10) | 3.86 s
    [Task 14/25]  Current/Best:    9.67/  11.60 GFLOPS | Progress: (8/10) | 7.08 s
    [Task 14/25]  Current/Best:   20.70/  20.70 GFLOPS | Progress: (10/10) | 7.85 s
    [Task 15/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/10) | 0.00 s
    [Task 15/25]  Current/Best:   12.40/  12.40 GFLOPS | Progress: (4/10) | 2.96 s
    [Task 15/25]  Current/Best:   18.90/  18.90 GFLOPS | Progress: (8/10) | 6.38 s
    [Task 15/25]  Current/Best:   13.83/  18.90 GFLOPS | Progress: (10/10) | 7.14 s
    [Task 16/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/10) | 0.00 s Done.
      Done.
-
    [Task 16/25]  Current/Best:    9.49/  15.85 GFLOPS | Progress: (4/10) | 2.90 s
    [Task 16/25]  Current/Best:   13.13/  20.91 GFLOPS | Progress: (8/10) | 4.53 s
    [Task 16/25]  Current/Best:    6.03/  20.91 GFLOPS | Progress: (10/10) | 5.70 s Done.
-
    [Task 17/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/10) | 0.00 s
    [Task 17/25]  Current/Best:    3.10/  11.93 GFLOPS | Progress: (4/10) | 4.81 s
    [Task 17/25]  Current/Best:    3.11/  24.29 GFLOPS | Progress: (8/10) | 7.42 s
    [Task 17/25]  Current/Best:   12.75/  24.29 GFLOPS | Progress: (10/10) | 8.59 s Done.
-
    [Task 18/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/10) | 0.00 s
    [Task 18/25]  Current/Best:   12.43/  16.49 GFLOPS | Progress: (4/10) | 3.55 s
    [Task 18/25]  Current/Best:   12.53/  18.23 GFLOPS | Progress: (8/10) | 6.10 s
    [Task 18/25]  Current/Best:   11.47/  18.23 GFLOPS | Progress: (10/10) | 6.86 s Done.
-
    [Task 19/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/10) | 0.00 s
    [Task 19/25]  Current/Best:   14.29/  14.29 GFLOPS | Progress: (4/10) | 5.71 s
    [Task 19/25]  Current/Best:   15.07/  18.40 GFLOPS | Progress: (8/10) | 7.84 s
    [Task 19/25]  Current/Best:    6.19/  23.47 GFLOPS | Progress: (10/10) | 9.90 s Done.
-
    [Task 20/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/10) | 0.00 s
    [Task 20/25]  Current/Best:   21.85/  21.85 GFLOPS | Progress: (4/10) | 3.56 s
    [Task 20/25]  Current/Best:    6.04/  21.85 GFLOPS | Progress: (8/10) | 7.38 s
    [Task 20/25]  Current/Best:    2.71/  21.85 GFLOPS | Progress: (10/10) | 9.16 s Done.
-
    [Task 21/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/10) | 0.00 s
    [Task 21/25]  Current/Best:   13.66/  16.36 GFLOPS | Progress: (4/10) | 2.66 s
    [Task 21/25]  Current/Best:   23.76/  23.76 GFLOPS | Progress: (8/10) | 3.94 s
    [Task 21/25]  Current/Best:    1.63/  23.76 GFLOPS | Progress: (10/10) | 5.16 s
    [Task 22/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/10) | 0.00 s
    [Task 22/25]  Current/Best:   18.89/  18.89 GFLOPS | Progress: (4/10) | 2.68 s
    [Task 22/25]  Current/Best:    6.02/  18.89 GFLOPS | Progress: (8/10) | 5.57 s
    [Task 22/25]  Current/Best:   18.12/  18.89 GFLOPS | Progress: (10/10) | 6.25 s Done.
-
    [Task 23/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/10) | 0.00 s
    [Task 23/25]  Current/Best:    5.39/  19.67 GFLOPS | Progress: (4/10) | 10.13 s
    [Task 23/25]  Current/Best:   11.44/  20.19 GFLOPS | Progress: (8/10) | 12.75 s
    [Task 23/25]  Current/Best:   20.62/  20.62 GFLOPS | Progress: (10/10) | 13.57 s Done.
-
    [Task 24/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/10) | 0.00 s
    [Task 24/25]  Current/Best:    4.41/   7.86 GFLOPS | Progress: (4/10) | 3.22 s
    [Task 24/25]  Current/Best:    4.36/   8.15 GFLOPS | Progress: (8/10) | 15.45 s
    [Task 24/25]  Current/Best:    4.05/   8.15 GFLOPS | Progress: (10/10) | 30.63 s
    [Task 25/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/10) | 0.00 s Done.
+
    [Task 16/25]  Current/Best:   16.05/  16.05 GFLOPS | Progress: (4/10) | 2.95 s
    [Task 16/25]  Current/Best:    7.42/  18.60 GFLOPS | Progress: (8/10) | 5.59 s
    [Task 16/25]  Current/Best:   16.47/  18.60 GFLOPS | Progress: (10/10) | 6.57 s Done.
+
    [Task 17/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/10) | 0.00 s
    [Task 17/25]  Current/Best:   12.23/  17.59 GFLOPS | Progress: (4/10) | 3.52 s
    [Task 17/25]  Current/Best:   15.37/  22.76 GFLOPS | Progress: (8/10) | 5.79 s
    [Task 17/25]  Current/Best:   19.95/  22.76 GFLOPS | Progress: (10/10) | 6.77 s Done.
+
    [Task 18/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/10) | 0.00 s
    [Task 18/25]  Current/Best:   15.50/  19.25 GFLOPS | Progress: (4/10) | 3.87 s
    [Task 18/25]  Current/Best:   17.22/  19.78 GFLOPS | Progress: (8/10) | 5.46 s
    [Task 18/25]  Current/Best:   12.43/  19.78 GFLOPS | Progress: (10/10) | 7.43 s Done.
+
    [Task 19/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/10) | 0.00 s
    [Task 19/25]  Current/Best:   20.72/  20.72 GFLOPS | Progress: (4/10) | 4.51 s
    [Task 19/25]  Current/Best:   11.45/  20.72 GFLOPS | Progress: (8/10) | 6.72 s
    [Task 19/25]  Current/Best:   22.12/  22.12 GFLOPS | Progress: (10/10) | 9.08 s Done.
+
    [Task 20/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/10) | 0.00 s
    [Task 20/25]  Current/Best:   17.28/  17.28 GFLOPS | Progress: (4/10) | 2.75 s
    [Task 20/25]  Current/Best:   19.15/  22.75 GFLOPS | Progress: (8/10) | 4.30 s
    [Task 20/25]  Current/Best:   14.72/  22.75 GFLOPS | Progress: (10/10) | 5.29 s
    [Task 21/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/10) | 0.00 s
    [Task 21/25]  Current/Best:    6.39/  11.46 GFLOPS | Progress: (4/10) | 4.17 s
    [Task 21/25]  Current/Best:   11.00/  18.59 GFLOPS | Progress: (8/10) | 6.58 s
    [Task 21/25]  Current/Best:   15.12/  18.59 GFLOPS | Progress: (10/10) | 8.98 s
    [Task 22/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/10) | 0.00 s
    [Task 22/25]  Current/Best:    2.08/  19.35 GFLOPS | Progress: (4/10) | 3.17 s
    [Task 22/25]  Current/Best:   17.34/  20.89 GFLOPS | Progress: (8/10) | 5.20 s
    [Task 22/25]  Current/Best:   15.29/  20.89 GFLOPS | Progress: (10/10) | 6.21
  s Done.
+
    [Task 23/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/10) | 0.00 s
    [Task 23/25]  Current/Best:   21.31/  22.71 GFLOPS | Progress: (4/10) | 2.70 s
    [Task 23/25]  Current/Best:    6.14/  22.71 GFLOPS | Progress: (8/10) | 5.32 s
    [Task 23/25]  Current/Best:   13.06/  22.71 GFLOPS | Progress: (10/10) | 6.54 s Done.
+
    [Task 24/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/10) | 0.00 s
    [Task 24/25]  Current/Best:    2.91/   2.91 GFLOPS | Progress: (4/10) | 33.30 s
    [Task 24/25]  Current/Best:    2.40/   3.75 GFLOPS | Progress: (8/10) | 36.71 s
    [Task 24/25]  Current/Best:    3.12/   3.75 GFLOPS | Progress: (10/10) | 37.34 s
    [Task 25/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/10) | 0.00 s Done.
      Done.
-
    [Task 25/25]  Current/Best:    1.55/   1.55 GFLOPS | Progress: (4/10) | 34.45 s
    [Task 25/25]  Current/Best:    6.07/   8.23 GFLOPS | Progress: (8/10) | 51.29 s
    [Task 25/25]  Current/Best:    0.00/   8.23 GFLOPS | Progress: (10/10) | 62.50 s
+     Done.
+
    [Task 25/25]  Current/Best:    2.94/   8.46 GFLOPS | Progress: (4/10) | 14.90 s
    [Task 25/25]  Current/Best:    3.96/   8.46 GFLOPS | Progress: (8/10) | 19.64 s
    [Task 25/25]  Current/Best:    2.94/   8.46 GFLOPS | Progress: (10/10) | 20.16 s
 
 
 The output from this tuning process will look something like this:
@@ -602,8 +602,8 @@ Verify that the optimized model runs and produces the same results:
 
  .. code-block:: none
 
-    class='n02123045 tabby, tabby cat' with probability=0.621104
-    class='n02123159 tiger cat' with probability=0.356378
+    class='n02123045 tabby, tabby cat' with probability=0.621103
+    class='n02123159 tiger cat' with probability=0.356379
     class='n02124075 Egyptian cat' with probability=0.019712
     class='n02129604 tiger, Panthera tigris' with probability=0.001215
     class='n04040759 radiator' with probability=0.000262
@@ -656,8 +656,8 @@ improvement in comparing the optimized model to the unoptimized model.
 
  .. code-block:: none
 
-    optimized: {'mean': 431.6472741200005, 'median': 431.64223475000085, 'std': 0.9637452451115474}
-    unoptimized: {'mean': 492.5690862900001, 'median': 492.4631355000031, 'std': 1.3102262374601348}
+    optimized: {'mean': 433.99948903000114, 'median': 433.8421493999988, 'std': 0.8912803859693517}
+    unoptimized: {'mean': 501.5567130400007, 'median': 501.14271980000353, 'std': 1.2693202754407098}
 
 
 
@@ -677,7 +677,7 @@ profiling/benchmarking.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 7 minutes  48.916 seconds)
+   **Total running time of the script:** ( 7 minutes  3.813 seconds)
 
 
 .. _sphx_glr_download_tutorial_autotvm_relay_x86.py:
diff --git a/docs/_sources/tutorial/cross_compilation_and_rpc.rst.txt b/docs/_sources/tutorial/cross_compilation_and_rpc.rst.txt
index 4b52d23c5..8df9571b7 100644
--- a/docs/_sources/tutorial/cross_compilation_and_rpc.rst.txt
+++ b/docs/_sources/tutorial/cross_compilation_and_rpc.rst.txt
@@ -235,7 +235,7 @@ device and returns the measured cost. Network overhead is excluded.
 
  .. code-block:: none
 
-    1.344e-07 secs/op
+    1.324e-07 secs/op
 
 
 
diff --git a/docs/_sources/tutorial/intro_topi.rst.txt b/docs/_sources/tutorial/intro_topi.rst.txt
index 8b799fb12..cdb8873c5 100644
--- a/docs/_sources/tutorial/intro_topi.rst.txt
+++ b/docs/_sources/tutorial/intro_topi.rst.txt
@@ -230,7 +230,7 @@ As you can see, scheduled stages of computation have been accumulated and we can
 
  .. code-block:: none
 
-    [stage(a, placeholder(a, 0x107bad80)), stage(b, placeholder(b, 0x229654d0)), stage(T_add, compute(T_add, body=[(a[ax0, ax1, ax2] + b[ax1, ax2])], axis=[iter_var(ax0, range(min=0, ext=100)), iter_var(ax1, range(min=0, ext=10)), iter_var(ax2, range(min=0, ext=10))], reduce_axis=[], tag=broadcast, attrs={})), stage(T_multiply, compute(T_multiply, body=[(a[ax0, ax1, ax2]*b[ax1, ax2])], axis=[iter_var(ax0, range(min=0, ext=100)), iter_var(ax1, range(min=0, ext=10)), iter_var(ax2, range(mi [...]
+    [stage(a, placeholder(a, 0x240f16c0)), stage(b, placeholder(b, 0x29565310)), stage(T_add, compute(T_add, body=[(a[ax0, ax1, ax2] + b[ax1, ax2])], axis=[iter_var(ax0, range(min=0, ext=100)), iter_var(ax1, range(min=0, ext=10)), iter_var(ax2, range(min=0, ext=10))], reduce_axis=[], tag=broadcast, attrs={})), stage(T_multiply, compute(T_multiply, body=[(a[ax0, ax1, ax2]*b[ax1, ax2])], axis=[iter_var(ax0, range(min=0, ext=100)), iter_var(ax1, range(min=0, ext=10)), iter_var(ax2, range(mi [...]
 
 
 
diff --git a/docs/_sources/tutorial/sg_execution_times.rst.txt b/docs/_sources/tutorial/sg_execution_times.rst.txt
index 5b8235d10..1bdae041f 100644
--- a/docs/_sources/tutorial/sg_execution_times.rst.txt
+++ b/docs/_sources/tutorial/sg_execution_times.rst.txt
@@ -5,17 +5,17 @@
 
 Computation times
 =================
-**10:34.152** total execution time for **tutorial** files:
+**10:06.284** total execution time for **tutorial** files:
 
-- **07:48.916**: :ref:`sphx_glr_tutorial_autotvm_relay_x86.py` (``autotvm_relay_x86.py``)
-- **01:00.960**: :ref:`sphx_glr_tutorial_tensor_expr_get_started.py` (``tensor_expr_get_started.py``)
-- **00:49.020**: :ref:`sphx_glr_tutorial_auto_scheduler_matmul_x86.py` (``auto_scheduler_matmul_x86.py``)
-- **00:26.868**: :ref:`sphx_glr_tutorial_autotvm_matmul_x86.py` (``autotvm_matmul_x86.py``)
-- **00:26.069**: :ref:`sphx_glr_tutorial_relay_quick_start.py` (``relay_quick_start.py``)
-- **00:01.264**: :ref:`sphx_glr_tutorial_tensor_ir_blitz_course.py` (``tensor_ir_blitz_course.py``)
-- **00:00.701**: :ref:`sphx_glr_tutorial_intro_topi.py` (``intro_topi.py``)
-- **00:00.216**: :ref:`sphx_glr_tutorial_cross_compilation_and_rpc.py` (``cross_compilation_and_rpc.py``)
-- **00:00.042**: :ref:`sphx_glr_tutorial_introduction.py` (``introduction.py``)
-- **00:00.035**: :ref:`sphx_glr_tutorial_tvmc_command_line_driver.py` (``tvmc_command_line_driver.py``)
-- **00:00.032**: :ref:`sphx_glr_tutorial_tvmc_python.py` (``tvmc_python.py``)
-- **00:00.030**: :ref:`sphx_glr_tutorial_install.py` (``install.py``)
+- **07:03.813**: :ref:`sphx_glr_tutorial_autotvm_relay_x86.py` (``autotvm_relay_x86.py``)
+- **01:11.447**: :ref:`sphx_glr_tutorial_auto_scheduler_matmul_x86.py` (``auto_scheduler_matmul_x86.py``)
+- **01:01.767**: :ref:`sphx_glr_tutorial_tensor_expr_get_started.py` (``tensor_expr_get_started.py``)
+- **00:27.017**: :ref:`sphx_glr_tutorial_relay_quick_start.py` (``relay_quick_start.py``)
+- **00:19.980**: :ref:`sphx_glr_tutorial_autotvm_matmul_x86.py` (``autotvm_matmul_x86.py``)
+- **00:01.097**: :ref:`sphx_glr_tutorial_tensor_ir_blitz_course.py` (``tensor_ir_blitz_course.py``)
+- **00:00.727**: :ref:`sphx_glr_tutorial_intro_topi.py` (``intro_topi.py``)
+- **00:00.222**: :ref:`sphx_glr_tutorial_cross_compilation_and_rpc.py` (``cross_compilation_and_rpc.py``)
+- **00:00.054**: :ref:`sphx_glr_tutorial_introduction.py` (``introduction.py``)
+- **00:00.054**: :ref:`sphx_glr_tutorial_tvmc_command_line_driver.py` (``tvmc_command_line_driver.py``)
+- **00:00.053**: :ref:`sphx_glr_tutorial_install.py` (``install.py``)
+- **00:00.052**: :ref:`sphx_glr_tutorial_tvmc_python.py` (``tvmc_python.py``)
diff --git a/docs/_sources/tutorial/tensor_expr_get_started.rst.txt b/docs/_sources/tutorial/tensor_expr_get_started.rst.txt
index d7770a2fe..16c916feb 100644
--- a/docs/_sources/tutorial/tensor_expr_get_started.rst.txt
+++ b/docs/_sources/tutorial/tensor_expr_get_started.rst.txt
@@ -243,7 +243,7 @@ helper function to run a profile of the TVM generated code.
 
  .. code-block:: none
 
-    Numpy running time: 0.000009
+    Numpy running time: 0.000007
     naive: 0.000007
 
 
@@ -334,7 +334,7 @@ compile and run this new schedule with the parallel operation applied:
 
  .. code-block:: none
 
-    parallel: 0.000007
+    parallel: 0.000006
 
 
 
@@ -436,10 +436,10 @@ We can now compare the different schedules
  .. code-block:: none
 
                 Operator                  Timing             Performance
-                   numpy    8.90588999936881e-06                     1.0
-                   naive              6.8638e-06      0.7707034334004194
-                parallel              7.0911e-06      0.7962258685546947
-                  vector             2.45479e-05      2.7563668540415156
+                   numpy    7.305290000658715e-06                    1.0
+                   naive    6.7811999999999996e-06    0.9282588370055865
+                parallel              6.1035e-06      0.8354904458891639
+                  vector              2.4503e-05       3.354144735909262
 
 
 
@@ -828,7 +828,7 @@ matrix multiplication.
 
  .. code-block:: none
 
-    Numpy running time: 0.018910
+    Numpy running time: 0.019036
 
 
 
@@ -884,7 +884,7 @@ optimizations.
 
  .. code-block:: none
 
-    none: 3.389160
+    none: 3.414679
 
 
 
@@ -982,7 +982,7 @@ schedule.
 
  .. code-block:: none
 
-    blocking: 0.315400
+    blocking: 0.320231
 
 
 
@@ -1073,7 +1073,7 @@ already cache friendly from our previous optimizations.
 
  .. code-block:: none
 
-    vectorization: 0.346792
+    vectorization: 0.341613
     @main = primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
       attr = {"from_legacy_te_schedule": True, "global_symbol": "main", "tir.noalias": True}
       buffers = {A: Buffer(A_2: Pointer(float32), float32, [1048576], []),
@@ -1144,7 +1144,7 @@ more cache friendly.
 
  .. code-block:: none
 
-    loop permutation: 0.115190
+    loop permutation: 0.136718
     @main = primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
       attr = {"from_legacy_te_schedule": True, "global_symbol": "main", "tir.noalias": True}
       buffers = {A: Buffer(A_2: Pointer(float32), float32, [1048576], []),
@@ -1240,7 +1240,7 @@ optimized schedule.
 
  .. code-block:: none
 
-    array packing: 0.110624
+    array packing: 0.111712
     @main = primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
       attr = {"from_legacy_te_schedule": True, "global_symbol": "main", "tir.noalias": True}
       buffers = {A: Buffer(A_2: Pointer(float32), float32, [1048576], []),
@@ -1330,7 +1330,7 @@ to `C` when all the block results are ready.
 
  .. code-block:: none
 
-    block caching: 0.110961
+    block caching: 0.112373
     @main = primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
       attr = {"from_legacy_te_schedule": True, "global_symbol": "main", "tir.noalias": True}
       buffers = {A: Buffer(A_2: Pointer(float32), float32, [1048576], []),
@@ -1413,7 +1413,7 @@ of thread-level parallelization.
 
  .. code-block:: none
 
-    parallelization: 0.144058
+    parallelization: 0.147064
     @main = primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
       attr = {"from_legacy_te_schedule": True, "global_symbol": "main", "tir.noalias": True}
       buffers = {A: Buffer(A_2: Pointer(float32), float32, [1048576], []),
@@ -1491,13 +1491,13 @@ working, we can compare the results.
  .. code-block:: none
 
                 Operator                  Timing             Performance
-                    none            3.3891596221                     1.0
-                blocking            0.3153996863      0.0930613253631799
-           vectorization     0.34679244389999997     0.10232402204919445
-        loop permutation     0.11519021680000001     0.03398784053984024
-           array packing     0.11062351150000001     0.03264039580155719
-           block caching     0.11096075920000001     0.03273990356678633
-         parallelization            0.1440581698    0.042505572431769466
+                    none      3.4146787949999995                     1.0
+                blocking            0.3202308548      0.0937806669455714
+           vectorization            0.3416128965     0.10004246870897854
+        loop permutation            0.1367184568     0.04003845310434243
+           array packing            0.1117116551     0.03271512836392567
+           block caching            0.1123728008     0.03290874707294395
+         parallelization     0.14706416309999998     0.04306822747584374
 
 
 
@@ -1534,7 +1534,7 @@ the computation for specific platforms.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 1 minutes  0.960 seconds)
+   **Total running time of the script:** ( 1 minutes  1.767 seconds)
 
 
 .. _sphx_glr_download_tutorial_tensor_expr_get_started.py:
diff --git a/docs/commit_hash b/docs/commit_hash
index 6f0b64a70..c030061d3 100644
--- a/docs/commit_hash
+++ b/docs/commit_hash
@@ -1 +1 @@
-fafabc96c1ba1a5f987c2402fcc2ce4d1bad5cc8
+7d9b7bbd503dc4365f803541f56edf1e34020925
diff --git a/docs/how_to/compile_models/from_mxnet.html b/docs/how_to/compile_models/from_mxnet.html
index c4a2be8ed..a00c7638f 100644
--- a/docs/how_to/compile_models/from_mxnet.html
+++ b/docs/how_to/compile_models/from_mxnet.html
@@ -400,7 +400,7 @@
 </div>
 <img alt="../../_images/sphx_glr_from_mxnet_001.png" class="sphx-glr-single-img" src="../../_images/sphx_glr_from_mxnet_001.png" />
 <p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Downloading /workspace/.mxnet/models/resnet18_v1-a0666292.zip6d3e2761-0eae-4f2a-aa04-e3b1d5eca3da from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/resnet18_v1-a0666292.zip...
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Downloading /workspace/.mxnet/models/resnet18_v1-a0666292.zip1c1becf4-3740-4016-b91f-8ea51d5c1905 from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/resnet18_v1-a0666292.zip...
 x (1, 3, 224, 224)
 </pre></div>
 </div>
diff --git a/docs/how_to/compile_models/from_paddle.html b/docs/how_to/compile_models/from_paddle.html
index f335c8a34..57aeb4d97 100644
--- a/docs/how_to/compile_models/from_paddle.html
+++ b/docs/how_to/compile_models/from_paddle.html
@@ -463,7 +463,7 @@ A quick solution is</p>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>TVM prediction top-1 id: 282, class name:  282: &#39;tiger cat&#39;,
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes  3.900 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes  6.727 seconds)</p>
 <div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-compile-models-from-paddle-py">
 <div class="sphx-glr-download docutils container">
 <p><a class="reference download internal" download="" href="../../_downloads/16269b77359771348d507395692524cf/from_paddle.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">from_paddle.py</span></code></a></p>
diff --git a/docs/how_to/compile_models/from_pytorch.html b/docs/how_to/compile_models/from_pytorch.html
index 958308e02..230474928 100644
--- a/docs/how_to/compile_models/from_pytorch.html
+++ b/docs/how_to/compile_models/from_pytorch.html
@@ -386,10 +386,8 @@ be unstable.</p>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Downloading: &quot;https://download.pytorch.org/models/resnet18-f37072fd.pth&quot; to /workspace/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth
 
   0%|          | 0.00/44.7M [00:00&lt;?, ?B/s]
-  7%|6         | 3.05M/44.7M [00:00&lt;00:01, 32.0MB/s]
- 17%|#7        | 7.61M/44.7M [00:00&lt;00:00, 41.0MB/s]
- 69%|######8   | 30.7M/44.7M [00:00&lt;00:00, 133MB/s]
-100%|##########| 44.7M/44.7M [00:00&lt;00:00, 131MB/s]
+ 43%|####3     | 19.2M/44.7M [00:00&lt;00:00, 201MB/s]
+100%|##########| 44.7M/44.7M [00:00&lt;00:00, 237MB/s]
 </pre></div>
 </div>
 </div>
diff --git a/docs/how_to/compile_models/from_tensorflow.html b/docs/how_to/compile_models/from_tensorflow.html
index b90a1b068..2513e9f57 100644
--- a/docs/how_to/compile_models/from_tensorflow.html
+++ b/docs/how_to/compile_models/from_tensorflow.html
@@ -606,6 +606,7 @@ banana (score = 0.00022)
 desk (score = 0.00019)
 </pre></div>
 </div>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes  4.689 seconds)</p>
 <div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-compile-models-from-tensorflow-py">
 <div class="sphx-glr-download docutils container">
 <p><a class="reference download internal" download="" href="../../_downloads/7f1d3d1b878694c201c614c807cdebc8/from_tensorflow.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">from_tensorflow.py</span></code></a></p>
diff --git a/docs/how_to/compile_models/sg_execution_times.html b/docs/how_to/compile_models/sg_execution_times.html
index 6d83df9b5..ecc30d390 100644
--- a/docs/how_to/compile_models/sg_execution_times.html
+++ b/docs/how_to/compile_models/sg_execution_times.html
@@ -300,17 +300,17 @@
             
   <div class="section" id="computation-times">
 <span id="sphx-glr-how-to-compile-models-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>04:38.468</strong> total execution time for <strong>how_to_compile_models</strong> files:</p>
+<p><strong>05:00.096</strong> total execution time for <strong>how_to_compile_models</strong> files:</p>
 <ul class="simple">
-<li><p><strong>01:03.900</strong>: <a class="reference internal" href="from_paddle.html#sphx-glr-how-to-compile-models-from-paddle-py"><span class="std std-ref">Compile PaddlePaddle Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_paddle.py</span></code>)</p></li>
-<li><p><strong>00:59.085</strong>: <a class="reference internal" href="from_tensorflow.html#sphx-glr-how-to-compile-models-from-tensorflow-py"><span class="std std-ref">Compile Tensorflow Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_tensorflow.py</span></code>)</p></li>
-<li><p><strong>00:55.277</strong>: <a class="reference internal" href="from_darknet.html#sphx-glr-how-to-compile-models-from-darknet-py"><span class="std std-ref">Compile YOLO-V2 and YOLO-V3 in DarkNet Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_darknet.py</span></code>)</p></li>
-<li><p><strong>00:25.181</strong>: <a class="reference internal" href="from_tflite.html#sphx-glr-how-to-compile-models-from-tflite-py"><span class="std std-ref">Compile TFLite Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_tflite.py</span></code>)</p></li>
-<li><p><strong>00:20.886</strong>: <a class="reference internal" href="from_coreml.html#sphx-glr-how-to-compile-models-from-coreml-py"><span class="std std-ref">Compile CoreML Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_coreml.py</span></code>)</p></li>
-<li><p><strong>00:20.807</strong>: <a class="reference internal" href="from_mxnet.html#sphx-glr-how-to-compile-models-from-mxnet-py"><span class="std std-ref">Compile MXNet Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_mxnet.py</span></code>)</p></li>
-<li><p><strong>00:18.207</strong>: <a class="reference internal" href="from_pytorch.html#sphx-glr-how-to-compile-models-from-pytorch-py"><span class="std std-ref">Compile PyTorch Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_pytorch.py</span></code>)</p></li>
-<li><p><strong>00:12.435</strong>: <a class="reference internal" href="from_keras.html#sphx-glr-how-to-compile-models-from-keras-py"><span class="std std-ref">Compile Keras Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_keras.py</span></code>)</p></li>
-<li><p><strong>00:02.691</strong>: <a class="reference internal" href="from_onnx.html#sphx-glr-how-to-compile-models-from-onnx-py"><span class="std std-ref">Compile ONNX Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_onnx.py</span></code>)</p></li>
+<li><p><strong>01:06.727</strong>: <a class="reference internal" href="from_paddle.html#sphx-glr-how-to-compile-models-from-paddle-py"><span class="std std-ref">Compile PaddlePaddle Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_paddle.py</span></code>)</p></li>
+<li><p><strong>01:04.689</strong>: <a class="reference internal" href="from_tensorflow.html#sphx-glr-how-to-compile-models-from-tensorflow-py"><span class="std std-ref">Compile Tensorflow Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_tensorflow.py</span></code>)</p></li>
+<li><p><strong>00:59.126</strong>: <a class="reference internal" href="from_darknet.html#sphx-glr-how-to-compile-models-from-darknet-py"><span class="std std-ref">Compile YOLO-V2 and YOLO-V3 in DarkNet Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_darknet.py</span></code>)</p></li>
+<li><p><strong>00:25.745</strong>: <a class="reference internal" href="from_tflite.html#sphx-glr-how-to-compile-models-from-tflite-py"><span class="std std-ref">Compile TFLite Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_tflite.py</span></code>)</p></li>
+<li><p><strong>00:25.278</strong>: <a class="reference internal" href="from_coreml.html#sphx-glr-how-to-compile-models-from-coreml-py"><span class="std std-ref">Compile CoreML Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_coreml.py</span></code>)</p></li>
+<li><p><strong>00:21.885</strong>: <a class="reference internal" href="from_mxnet.html#sphx-glr-how-to-compile-models-from-mxnet-py"><span class="std std-ref">Compile MXNet Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_mxnet.py</span></code>)</p></li>
+<li><p><strong>00:19.756</strong>: <a class="reference internal" href="from_pytorch.html#sphx-glr-how-to-compile-models-from-pytorch-py"><span class="std std-ref">Compile PyTorch Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_pytorch.py</span></code>)</p></li>
+<li><p><strong>00:14.141</strong>: <a class="reference internal" href="from_keras.html#sphx-glr-how-to-compile-models-from-keras-py"><span class="std std-ref">Compile Keras Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_keras.py</span></code>)</p></li>
+<li><p><strong>00:02.749</strong>: <a class="reference internal" href="from_onnx.html#sphx-glr-how-to-compile-models-from-onnx-py"><span class="std std-ref">Compile ONNX Models</span></a> (<code class="docutils literal notranslate"><span class="pre">from_onnx.py</span></code>)</p></li>
 </ul>
 </div>
 
diff --git a/docs/how_to/deploy_models/deploy_model_on_android.html b/docs/how_to/deploy_models/deploy_model_on_android.html
index 63da494c4..20cc0325f 100644
--- a/docs/how_to/deploy_models/deploy_model_on_android.html
+++ b/docs/how_to/deploy_models/deploy_model_on_android.html
@@ -622,7 +622,7 @@ to the remote android device.</p>
 Evaluate inference time cost...
 Execution time summary:
  mean (ms)   median (ms)    max (ms)     min (ms)     std (ms)
-  15.9951      15.9650      16.4304      15.8805       0.1509
+  16.4276      16.4296      16.5274      16.3232       0.0568
 </pre></div>
 </div>
 </div>
diff --git a/docs/how_to/deploy_models/deploy_object_detection_pytorch.html b/docs/how_to/deploy_models/deploy_object_detection_pytorch.html
index 4d6adf963..15a34d18b 100644
--- a/docs/how_to/deploy_models/deploy_object_detection_pytorch.html
+++ b/docs/how_to/deploy_models/deploy_object_detection_pytorch.html
@@ -409,15 +409,21 @@ be unstable.</p>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Downloading: &quot;https://download.pytorch.org/models/maskrcnn_resnet50_fpn_coco-bf2d0c1e.pth&quot; to /workspace/.cache/torch/hub/checkpoints/maskrcnn_resnet50_fpn_coco-bf2d0c1e.pth
 
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 /usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:3878: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
   for i in range(dim)
 /usr/local/lib/python3.7/dist-packages/torchvision/models/detection/anchor_utils.py:127: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the &#39;trunc&#39; function NOT &#39;floor&#39;). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode=&#39;trunc&#39;), or for actual floor division, use torch.div(a, b, rounding_mode=&#39;floor&#39;).
@@ -510,7 +516,7 @@ torchvision rcnn models.</p>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Get 9 valid boxes
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 3 minutes  0.840 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 3 minutes  18.369 seconds)</p>
 <div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-deploy-models-deploy-object-detection-pytorch-py">
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 <p><a class="reference download internal" download="" href="../../_downloads/7795da4b258c8feff986668b95ef57ad/deploy_object_detection_pytorch.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">deploy_object_detection_pytorch.py</span></code></a></p>
diff --git a/docs/how_to/deploy_models/deploy_prequantized.html b/docs/how_to/deploy_models/deploy_prequantized.html
index 7cb06b79e..4e52873ab 100644
--- a/docs/how_to/deploy_models/deploy_prequantized.html
+++ b/docs/how_to/deploy_models/deploy_prequantized.html
@@ -450,7 +450,7 @@ training. Other models require a full post training calibration.</p>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Downloading: &quot;https://download.pytorch.org/models/mobilenet_v2-b0353104.pth&quot; to /workspace/.cache/torch/hub/checkpoints/mobilenet_v2-b0353104.pth
 
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@@ -539,7 +539,7 @@ output values are identical out of 1000 outputs from mobilenet v2.</p>
 <p class="sphx-glr-script-out">Out:</p>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time summary:
  mean (ms)   median (ms)    max (ms)     min (ms)     std (ms)
-  90.1193      90.0943      91.0812      89.8953       0.1627
+  90.7121      90.4435      97.5283      90.3172       1.1237
 </pre></div>
 </div>
 <div class="admonition note">
@@ -578,7 +578,7 @@ This includes support for the VNNI 8 bit dot product instruction (CascadeLake or
 <div class="section" id="deploy-a-quantized-tflite-model">
 <h2>Deploy a quantized TFLite Model<a class="headerlink" href="#deploy-a-quantized-tflite-model" title="Permalink to this headline">¶</a></h2>
 <p>TODO</p>
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+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes  8.361 seconds)</p>
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 <p><a class="reference download internal" download="" href="../../_downloads/fb8217c13f4351224c6cf3aacf1a87fc/deploy_prequantized.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">deploy_prequantized.py</span></code></a></p>
diff --git a/docs/how_to/deploy_models/deploy_prequantized_tflite.html b/docs/how_to/deploy_models/deploy_prequantized_tflite.html
index 33ad6bafb..e0afba291 100644
--- a/docs/how_to/deploy_models/deploy_prequantized_tflite.html
+++ b/docs/how_to/deploy_models/deploy_prequantized_tflite.html
@@ -540,7 +540,7 @@ TFLite Top-5 labels: [387 102 386 341 349]
 <p class="sphx-glr-script-out">Out:</p>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time summary:
  mean (ms)   median (ms)    max (ms)     min (ms)     std (ms)
-  119.4266     119.4160     120.6976     118.3389      0.4110
+  122.1462     122.1530     122.7372     121.4437      0.2733
 </pre></div>
 </div>
 <div class="admonition note">
@@ -568,7 +568,7 @@ network for ARM CPU</span></a>.</p></li>
 </ul>
 </div></blockquote>
 </div>
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+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes  54.368 seconds)</p>
 <div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-deploy-models-deploy-prequantized-tflite-py">
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 <p><a class="reference download internal" download="" href="../../_downloads/56691c7a27d45da61d112276334640d3/deploy_prequantized_tflite.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">deploy_prequantized_tflite.py</span></code></a></p>
diff --git a/docs/how_to/deploy_models/deploy_quantized.html b/docs/how_to/deploy_models/deploy_quantized.html
index f0c135cdf..115ebf2c6 100644
--- a/docs/how_to/deploy_models/deploy_quantized.html
+++ b/docs/how_to/deploy_models/deploy_quantized.html
@@ -480,7 +480,7 @@ for calibration. But the accuracy might be impacted.</p>
   DeprecationWarning,
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes  9.426 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes  16.170 seconds)</p>
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 <p><a class="reference download internal" download="" href="../../_downloads/7810ecf51bfc05f7d5e8a400ac3e815d/deploy_quantized.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">deploy_quantized.py</span></code></a></p>
diff --git a/docs/how_to/deploy_models/deploy_ssd_gluoncv.html b/docs/how_to/deploy_models/deploy_ssd_gluoncv.html
index b4682eeea..eeeca8335 100644
--- a/docs/how_to/deploy_models/deploy_ssd_gluoncv.html
+++ b/docs/how_to/deploy_models/deploy_ssd_gluoncv.html
@@ -415,26 +415,25 @@ to your device.</p>
 Downloading /workspace/.mxnet/models/ssd_512_resnet50_v1_voc-9c8b225a.zip from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/ssd_512_resnet50_v1_voc-9c8b225a.zip...
 
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 </div>
 <p>Create TVM runtime and do inference
@@ -474,7 +473,7 @@ Downloading /workspace/.mxnet/models/ssd_512_resnet50_v1_voc-9c8b225a.zip from h
 </pre></div>
 </div>
 <img alt="../../_images/sphx_glr_deploy_ssd_gluoncv_001.png" class="sphx-glr-single-img" src="../../_images/sphx_glr_deploy_ssd_gluoncv_001.png" />
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes  21.536 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes  31.083 seconds)</p>
 <div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-deploy-models-deploy-ssd-gluoncv-py">
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 <p><a class="reference download internal" download="" href="../../_downloads/cccb17d28e5e8b2e94ea8cd5ec59f6ed/deploy_ssd_gluoncv.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">deploy_ssd_gluoncv.py</span></code></a></p>
diff --git a/docs/how_to/deploy_models/sg_execution_times.html b/docs/how_to/deploy_models/sg_execution_times.html
index 2d28d8d69..18824451d 100644
--- a/docs/how_to/deploy_models/sg_execution_times.html
+++ b/docs/how_to/deploy_models/sg_execution_times.html
@@ -300,16 +300,16 @@
             
   <div class="section" id="computation-times">
 <span id="sphx-glr-how-to-deploy-models-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>10:19.507</strong> total execution time for <strong>how_to_deploy_models</strong> files:</p>
+<p><strong>11:00.202</strong> total execution time for <strong>how_to_deploy_models</strong> files:</p>
 <ul class="simple">
-<li><p><strong>03:00.840</strong>: <a class="reference internal" href="deploy_object_detection_pytorch.html#sphx-glr-how-to-deploy-models-deploy-object-detection-pytorch-py"><span class="std std-ref">Compile PyTorch Object Detection Models</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_object_detection_pytorch.py</span></code>)</p></li>
-<li><p><strong>02:21.536</strong>: <a class="reference internal" href="deploy_ssd_gluoncv.html#sphx-glr-how-to-deploy-models-deploy-ssd-gluoncv-py"><span class="std std-ref">Deploy Single Shot Multibox Detector(SSD) model</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_ssd_gluoncv.py</span></code>)</p></li>
-<li><p><strong>01:54.100</strong>: <a class="reference internal" href="deploy_prequantized_tflite.html#sphx-glr-how-to-deploy-models-deploy-prequantized-tflite-py"><span class="std std-ref">Deploy a Framework-prequantized Model with TVM - Part 3 (TFLite)</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_prequantized_tflite.py</span></code>)</p></li>
-<li><p><strong>01:09.426</strong>: <a class="reference internal" href="deploy_quantized.html#sphx-glr-how-to-deploy-models-deploy-quantized-py"><span class="std std-ref">Deploy a Quantized Model on Cuda</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_quantized.py</span></code>)</p></li>
-<li><p><strong>01:04.586</strong>: <a class="reference internal" href="deploy_prequantized.html#sphx-glr-how-to-deploy-models-deploy-prequantized-py"><span class="std std-ref">Deploy a Framework-prequantized Model with TVM</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_prequantized.py</span></code>)</p></li>
-<li><p><strong>00:27.722</strong>: <a class="reference internal" href="deploy_model_on_android.html#sphx-glr-how-to-deploy-models-deploy-model-on-android-py"><span class="std std-ref">Deploy the Pretrained Model on Android</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_android.py</span></code>)</p></li>
-<li><p><strong>00:21.110</strong>: <a class="reference internal" href="deploy_model_on_rasp.html#sphx-glr-how-to-deploy-models-deploy-model-on-rasp-py"><span class="std std-ref">Deploy the Pretrained Model on Raspberry Pi</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_rasp.py</span></code>)</p></li>
-<li><p><strong>00:00.188</strong>: <a class="reference internal" href="deploy_sparse.html#sphx-glr-how-to-deploy-models-deploy-sparse-py"><span class="std std-ref">Deploy a Hugging Face Pruned Model on CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_sparse.py</span></code>)</p></li>
+<li><p><strong>03:18.369</strong>: <a class="reference internal" href="deploy_object_detection_pytorch.html#sphx-glr-how-to-deploy-models-deploy-object-detection-pytorch-py"><span class="std std-ref">Compile PyTorch Object Detection Models</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_object_detection_pytorch.py</span></code>)</p></li>
+<li><p><strong>02:31.083</strong>: <a class="reference internal" href="deploy_ssd_gluoncv.html#sphx-glr-how-to-deploy-models-deploy-ssd-gluoncv-py"><span class="std std-ref">Deploy Single Shot Multibox Detector(SSD) model</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_ssd_gluoncv.py</span></code>)</p></li>
+<li><p><strong>01:54.368</strong>: <a class="reference internal" href="deploy_prequantized_tflite.html#sphx-glr-how-to-deploy-models-deploy-prequantized-tflite-py"><span class="std std-ref">Deploy a Framework-prequantized Model with TVM - Part 3 (TFLite)</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_prequantized_tflite.py</span></code>)</p></li>
+<li><p><strong>01:16.170</strong>: <a class="reference internal" href="deploy_quantized.html#sphx-glr-how-to-deploy-models-deploy-quantized-py"><span class="std std-ref">Deploy a Quantized Model on Cuda</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_quantized.py</span></code>)</p></li>
+<li><p><strong>01:08.361</strong>: <a class="reference internal" href="deploy_prequantized.html#sphx-glr-how-to-deploy-models-deploy-prequantized-py"><span class="std std-ref">Deploy a Framework-prequantized Model with TVM</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_prequantized.py</span></code>)</p></li>
+<li><p><strong>00:29.370</strong>: <a class="reference internal" href="deploy_model_on_android.html#sphx-glr-how-to-deploy-models-deploy-model-on-android-py"><span class="std std-ref">Deploy the Pretrained Model on Android</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_android.py</span></code>)</p></li>
+<li><p><strong>00:22.282</strong>: <a class="reference internal" href="deploy_model_on_rasp.html#sphx-glr-how-to-deploy-models-deploy-model-on-rasp-py"><span class="std std-ref">Deploy the Pretrained Model on Raspberry Pi</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_rasp.py</span></code>)</p></li>
+<li><p><strong>00:00.197</strong>: <a class="reference internal" href="deploy_sparse.html#sphx-glr-how-to-deploy-models-deploy-sparse-py"><span class="std std-ref">Deploy a Hugging Face Pruned Model on CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_sparse.py</span></code>)</p></li>
 </ul>
 </div>
 
diff --git a/docs/how_to/extend_tvm/bring_your_own_datatypes.html b/docs/how_to/extend_tvm/bring_your_own_datatypes.html
index ab35f1c7b..f62787852 100644
--- a/docs/how_to/extend_tvm/bring_your_own_datatypes.html
+++ b/docs/how_to/extend_tvm/bring_your_own_datatypes.html
@@ -588,7 +588,7 @@ In this alpha state of the Bring Your Own Datatypes framework, we have not imple
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Downloading /workspace/.mxnet/models/mobilenet0.25-9f83e440.zip1c0edb4c-2721-4485-a82f-207087b6065e from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/mobilenet0.25-9f83e440.zip...
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Downloading /workspace/.mxnet/models/mobilenet0.25-9f83e440.zip89da06ac-6dbc-4b53-a94a-d688ee318fc2 from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/mobilenet0.25-9f83e440.zip...
 </pre></div>
 </div>
 <p>It’s easy to execute MobileNet with native TVM:</p>
diff --git a/docs/how_to/extend_tvm/sg_execution_times.html b/docs/how_to/extend_tvm/sg_execution_times.html
index 539da0f14..9efbefba3 100644
--- a/docs/how_to/extend_tvm/sg_execution_times.html
+++ b/docs/how_to/extend_tvm/sg_execution_times.html
@@ -300,12 +300,12 @@
             
   <div class="section" id="computation-times">
 <span id="sphx-glr-how-to-extend-tvm-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>00:37.772</strong> total execution time for <strong>how_to_extend_tvm</strong> files:</p>
+<p><strong>00:39.897</strong> total execution time for <strong>how_to_extend_tvm</strong> files:</p>
 <ul class="simple">
-<li><p><strong>00:34.314</strong>: <a class="reference internal" href="bring_your_own_datatypes.html#sphx-glr-how-to-extend-tvm-bring-your-own-datatypes-py"><span class="std std-ref">Bring Your Own Datatypes to TVM</span></a> (<code class="docutils literal notranslate"><span class="pre">bring_your_own_datatypes.py</span></code>)</p></li>
-<li><p><strong>00:02.210</strong>: <a class="reference internal" href="use_pass_instrument.html#sphx-glr-how-to-extend-tvm-use-pass-instrument-py"><span class="std std-ref">How to Use TVM Pass Instrument</span></a> (<code class="docutils literal notranslate"><span class="pre">use_pass_instrument.py</span></code>)</p></li>
-<li><p><strong>00:01.048</strong>: <a class="reference internal" href="use_pass_infra.html#sphx-glr-how-to-extend-tvm-use-pass-infra-py"><span class="std std-ref">How to Use TVM Pass Infra</span></a> (<code class="docutils literal notranslate"><span class="pre">use_pass_infra.py</span></code>)</p></li>
-<li><p><strong>00:00.200</strong>: <a class="reference internal" href="low_level_custom_pass.html#sphx-glr-how-to-extend-tvm-low-level-custom-pass-py"><span class="std std-ref">Writing a Customized Pass</span></a> (<code class="docutils literal notranslate"><span class="pre">low_level_custom_pass.py</span></code>)</p></li>
+<li><p><strong>00:36.255</strong>: <a class="reference internal" href="bring_your_own_datatypes.html#sphx-glr-how-to-extend-tvm-bring-your-own-datatypes-py"><span class="std std-ref">Bring Your Own Datatypes to TVM</span></a> (<code class="docutils literal notranslate"><span class="pre">bring_your_own_datatypes.py</span></code>)</p></li>
+<li><p><strong>00:02.342</strong>: <a class="reference internal" href="use_pass_instrument.html#sphx-glr-how-to-extend-tvm-use-pass-instrument-py"><span class="std std-ref">How to Use TVM Pass Instrument</span></a> (<code class="docutils literal notranslate"><span class="pre">use_pass_instrument.py</span></code>)</p></li>
+<li><p><strong>00:01.093</strong>: <a class="reference internal" href="use_pass_infra.html#sphx-glr-how-to-extend-tvm-use-pass-infra-py"><span class="std std-ref">How to Use TVM Pass Infra</span></a> (<code class="docutils literal notranslate"><span class="pre">use_pass_infra.py</span></code>)</p></li>
+<li><p><strong>00:00.207</strong>: <a class="reference internal" href="low_level_custom_pass.html#sphx-glr-how-to-extend-tvm-low-level-custom-pass-py"><span class="std std-ref">Writing a Customized Pass</span></a> (<code class="docutils literal notranslate"><span class="pre">low_level_custom_pass.py</span></code>)</p></li>
 </ul>
 </div>
 
diff --git a/docs/how_to/extend_tvm/use_pass_instrument.html b/docs/how_to/extend_tvm/use_pass_instrument.html
index c55c07a65..4042e29be 100644
--- a/docs/how_to/extend_tvm/use_pass_instrument.html
+++ b/docs/how_to/extend_tvm/use_pass_instrument.html
@@ -486,10 +486,10 @@ profile the execution time of each passes.</p>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Printing results of timing profile...
-InferType: 5825us [5825us] (44.98%; 44.98%)
-FoldScaleAxis: 7124us [2us] (55.02%; 55.02%)
-        FoldConstant: 7122us [1502us] (55.00%; 99.97%)
-                InferType: 5620us [5620us] (43.40%; 78.92%)
+InferType: 5965us [5965us] (45.27%; 45.27%)
+FoldScaleAxis: 7211us [2us] (54.73%; 54.73%)
+        FoldConstant: 7209us [1463us] (54.71%; 99.97%)
+                InferType: 5745us [5745us] (43.60%; 79.70%)
 </pre></div>
 </div>
 </div>
@@ -512,10 +512,10 @@ Refer to following sections and <a class="reference internal" href="../../refere
 </div>
 <p class="sphx-glr-script-out">Out:</p>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Printing results of timing profile...
-InferType: 5729us [5729us] (44.57%; 44.57%)
-FoldScaleAxis: 7124us [2us] (55.43%; 55.43%)
-        FoldConstant: 7122us [1488us] (55.41%; 99.98%)
-                InferType: 5634us [5634us] (43.83%; 79.10%)
+InferType: 5874us [5874us] (44.70%; 44.70%)
+FoldScaleAxis: 7267us [2us] (55.30%; 55.30%)
+        FoldConstant: 7265us [1490us] (55.28%; 99.97%)
+                InferType: 5774us [5774us] (43.94%; 79.48%)
 </pre></div>
 </div>
 <p>Register empty list to clear existing instruments.</p>
diff --git a/docs/how_to/optimize_operators/opt_conv_cuda.html b/docs/how_to/optimize_operators/opt_conv_cuda.html
index 54acaef0b..263874804 100644
--- a/docs/how_to/optimize_operators/opt_conv_cuda.html
+++ b/docs/how_to/optimize_operators/opt_conv_cuda.html
@@ -534,7 +534,7 @@ latency of convolution.</p>
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Convolution: 44.464928 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Convolution: 45.031039 ms
 </pre></div>
 </div>
 <div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-optimize-operators-opt-conv-cuda-py">
diff --git a/docs/how_to/optimize_operators/opt_conv_tensorcore.html b/docs/how_to/optimize_operators/opt_conv_tensorcore.html
index 8e3a25c41..13b69eda2 100644
--- a/docs/how_to/optimize_operators/opt_conv_tensorcore.html
+++ b/docs/how_to/optimize_operators/opt_conv_tensorcore.html
@@ -876,7 +876,7 @@ be able to run on our build server</p>
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>conv2d with tensor core: 6.923151 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>conv2d with tensor core: 10.467079 ms
 </pre></div>
 </div>
 </div>
diff --git a/docs/how_to/optimize_operators/opt_gemm.html b/docs/how_to/optimize_operators/opt_gemm.html
index 7d80ed69f..2589cb2d7 100644
--- a/docs/how_to/optimize_operators/opt_gemm.html
+++ b/docs/how_to/optimize_operators/opt_gemm.html
@@ -431,8 +431,8 @@ Then we write a baseline implementation, the simplest way to write a matrix mult
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Numpy running time: 0.018355
-Baseline: 3.388145
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Numpy running time: 0.019652
+Baseline: 3.352839
 </pre></div>
 </div>
 <p>In TVM, we can always inspect lower level IR to debug or optimize our schedule.
@@ -493,7 +493,7 @@ fill 32 * 32 * sizeof(float) which is 4KB in the cache whose total size is 32KB
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt1: 0.304776
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt1: 0.320437
 </pre></div>
 </div>
 <p>Here is the generated IR after blocking.</p>
@@ -561,7 +561,7 @@ vastly.</p>
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt2: 0.339592
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt2: 0.341972
 </pre></div>
 </div>
 <p>Here is the generated IR after vectorization.</p>
@@ -623,7 +623,7 @@ the access pattern for A matrix is more cache friendly.</p>
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt3: 0.116929
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt3: 0.134355
 </pre></div>
 </div>
 <p>Here is the generated IR after loop permutation.</p>
@@ -707,7 +707,7 @@ flattening.</p>
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt4: 0.110666
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt4: 0.112803
 </pre></div>
 </div>
 <p>Here is the generated IR after array packing.</p>
@@ -794,7 +794,7 @@ write to C when all the block results are ready.</p>
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt5: 0.110533
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt5: 0.113798
 </pre></div>
 </div>
 <p>Here is the generated IR after blocking.</p>
@@ -885,7 +885,7 @@ write to C when all the block results are ready.</p>
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt6: 0.145002
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt6: 0.147472
 </pre></div>
 </div>
 <p>Here is the generated IR after parallelization.</p>
diff --git a/docs/how_to/optimize_operators/sg_execution_times.html b/docs/how_to/optimize_operators/sg_execution_times.html
index ab36ebd38..cd2e5dc13 100644
--- a/docs/how_to/optimize_operators/sg_execution_times.html
+++ b/docs/how_to/optimize_operators/sg_execution_times.html
@@ -300,11 +300,11 @@
             
   <div class="section" id="computation-times">
 <span id="sphx-glr-how-to-optimize-operators-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>00:34.973</strong> total execution time for <strong>how_to_optimize_operators</strong> files:</p>
+<p><strong>00:35.818</strong> total execution time for <strong>how_to_optimize_operators</strong> files:</p>
 <ul class="simple">
-<li><p><strong>00:32.353</strong>: <a class="reference internal" href="opt_gemm.html#sphx-glr-how-to-optimize-operators-opt-gemm-py"><span class="std std-ref">How to optimize GEMM on CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">opt_gemm.py</span></code>)</p></li>
-<li><p><strong>00:01.398</strong>: <a class="reference internal" href="opt_conv_tensorcore.html#sphx-glr-how-to-optimize-operators-opt-conv-tensorcore-py"><span class="std std-ref">How to optimize convolution using TensorCores</span></a> (<code class="docutils literal notranslate"><span class="pre">opt_conv_tensorcore.py</span></code>)</p></li>
-<li><p><strong>00:01.221</strong>: <a class="reference internal" href="opt_conv_cuda.html#sphx-glr-how-to-optimize-operators-opt-conv-cuda-py"><span class="std std-ref">How to optimize convolution on GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">opt_conv_cuda.py</span></code>)</p></li>
+<li><p><strong>00:33.099</strong>: <a class="reference internal" href="opt_gemm.html#sphx-glr-how-to-optimize-operators-opt-gemm-py"><span class="std std-ref">How to optimize GEMM on CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">opt_gemm.py</span></code>)</p></li>
+<li><p><strong>00:01.461</strong>: <a class="reference internal" href="opt_conv_tensorcore.html#sphx-glr-how-to-optimize-operators-opt-conv-tensorcore-py"><span class="std std-ref">How to optimize convolution using TensorCores</span></a> (<code class="docutils literal notranslate"><span class="pre">opt_conv_tensorcore.py</span></code>)</p></li>
+<li><p><strong>00:01.258</strong>: <a class="reference internal" href="opt_conv_cuda.html#sphx-glr-how-to-optimize-operators-opt-conv-cuda-py"><span class="std std-ref">How to optimize convolution on GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">opt_conv_cuda.py</span></code>)</p></li>
 </ul>
 </div>
 
diff --git a/docs/how_to/tune_with_autoscheduler/sg_execution_times.html b/docs/how_to/tune_with_autoscheduler/sg_execution_times.html
index 35d4f2054..3a535bfb3 100644
--- a/docs/how_to/tune_with_autoscheduler/sg_execution_times.html
+++ b/docs/how_to/tune_with_autoscheduler/sg_execution_times.html
@@ -300,14 +300,14 @@
             
   <div class="section" id="computation-times">
 <span id="sphx-glr-how-to-tune-with-autoscheduler-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>04:53.427</strong> total execution time for <strong>how_to_tune_with_autoscheduler</strong> files:</p>
+<p><strong>04:58.445</strong> total execution time for <strong>how_to_tune_with_autoscheduler</strong> files:</p>
 <ul class="simple">
-<li><p><strong>02:20.154</strong>: <a class="reference internal" href="tune_conv2d_layer_cuda.html#sphx-glr-how-to-tune-with-autoscheduler-tune-conv2d-layer-cuda-py"><span class="std std-ref">Auto-scheduling a Convolution Layer for GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_conv2d_layer_cuda.py</span></code>)</p></li>
-<li><p><strong>01:18.957</strong>: <a class="reference internal" href="tune_network_x86.html#sphx-glr-how-to-tune-with-autoscheduler-tune-network-x86-py"><span class="std std-ref">Auto-scheduling a Neural Network for x86 CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_network_x86.py</span></code>)</p></li>
-<li><p><strong>00:40.191</strong>: <a class="reference internal" href="tune_network_cuda.html#sphx-glr-how-to-tune-with-autoscheduler-tune-network-cuda-py"><span class="std std-ref">Auto-scheduling a Neural Network for NVIDIA GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_network_cuda.py</span></code>)</p></li>
-<li><p><strong>00:17.223</strong>: <a class="reference internal" href="tune_sparse_x86.html#sphx-glr-how-to-tune-with-autoscheduler-tune-sparse-x86-py"><span class="std std-ref">Auto-scheduling Sparse Matrix Multiplication on CPU with Custom Sketch Rule</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_sparse_x86.py</span></code>)</p></li>
-<li><p><strong>00:08.638</strong>: <a class="reference internal" href="tune_network_mali.html#sphx-glr-how-to-tune-with-autoscheduler-tune-network-mali-py"><span class="std std-ref">Auto-scheduling a Neural Network for mali GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_network_mali.py</span></code>)</p></li>
-<li><p><strong>00:08.264</strong>: <a class="reference internal" href="tune_network_arm.html#sphx-glr-how-to-tune-with-autoscheduler-tune-network-arm-py"><span class="std std-ref">Auto-scheduling a Neural Network for ARM CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_network_arm.py</span></code>)</p></li>
+<li><p><strong>02:21.500</strong>: <a class="reference internal" href="tune_conv2d_layer_cuda.html#sphx-glr-how-to-tune-with-autoscheduler-tune-conv2d-layer-cuda-py"><span class="std std-ref">Auto-scheduling a Convolution Layer for GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_conv2d_layer_cuda.py</span></code>)</p></li>
+<li><p><strong>01:21.317</strong>: <a class="reference internal" href="tune_network_x86.html#sphx-glr-how-to-tune-with-autoscheduler-tune-network-x86-py"><span class="std std-ref">Auto-scheduling a Neural Network for x86 CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_network_x86.py</span></code>)</p></li>
+<li><p><strong>00:41.395</strong>: <a class="reference internal" href="tune_network_cuda.html#sphx-glr-how-to-tune-with-autoscheduler-tune-network-cuda-py"><span class="std std-ref">Auto-scheduling a Neural Network for NVIDIA GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_network_cuda.py</span></code>)</p></li>
+<li><p><strong>00:16.070</strong>: <a class="reference internal" href="tune_sparse_x86.html#sphx-glr-how-to-tune-with-autoscheduler-tune-sparse-x86-py"><span class="std std-ref">Auto-scheduling Sparse Matrix Multiplication on CPU with Custom Sketch Rule</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_sparse_x86.py</span></code>)</p></li>
+<li><p><strong>00:09.281</strong>: <a class="reference internal" href="tune_network_mali.html#sphx-glr-how-to-tune-with-autoscheduler-tune-network-mali-py"><span class="std std-ref">Auto-scheduling a Neural Network for mali GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_network_mali.py</span></code>)</p></li>
+<li><p><strong>00:08.880</strong>: <a class="reference internal" href="tune_network_arm.html#sphx-glr-how-to-tune-with-autoscheduler-tune-network-arm-py"><span class="std std-ref">Auto-scheduling a Neural Network for ARM CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_network_arm.py</span></code>)</p></li>
 </ul>
 </div>
 
diff --git a/docs/how_to/tune_with_autoscheduler/tune_conv2d_layer_cuda.html b/docs/how_to/tune_with_autoscheduler/tune_conv2d_layer_cuda.html
index e6a04fc92..9d8fa4daf 100644
--- a/docs/how_to/tune_with_autoscheduler/tune_conv2d_layer_cuda.html
+++ b/docs/how_to/tune_with_autoscheduler/tune_conv2d_layer_cuda.html
@@ -469,350 +469,498 @@ cooperative fetching, unrolling and operator fusion.</p>
              bias: Buffer(bias_2: Pointer(float32), float32, [512], []),
              compute: Buffer(compute_2: Pointer(float32), float32, [25088], [])}
   buffer_map = {data_1: data, kernel_1: kernel, bias_1: bias, compute_1: compute} {
-  attr [IterVar(blockIdx.x: int32, (nullptr), &quot;ThreadIndex&quot;, &quot;blockIdx.x&quot;)] &quot;thread_extent&quot; = 32;
-  allocate(conv2d_nchw: Pointer(local float32), float32, [28]), storage_scope = local;
-  allocate(pad_temp.shared: Pointer(shared float32), float32, [504]), storage_scope = shared;
+  attr [IterVar(blockIdx.x: int32, (nullptr), &quot;ThreadIndex&quot;, &quot;blockIdx.x&quot;)] &quot;thread_extent&quot; = 224;
+  allocate(conv2d_nchw: Pointer(local float32), float32, [14]), storage_scope = local;
+  allocate(pad_temp.shared: Pointer(shared float32), float32, [72]), storage_scope = shared;
   allocate(kernel.shared: Pointer(shared float32), float32, [384]), storage_scope = shared;
-  attr [IterVar(threadIdx.x: int32, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 28 {
-    conv2d_nchw_1: Buffer(conv2d_nchw, float32, [16], [], scope=&quot;local&quot;, align=16)[0] = 0f32
-    conv2d_nchw_1[4] = 0f32
-    conv2d_nchw_1[8] = 0f32
-    conv2d_nchw_1[12] = 0f32
-    conv2d_nchw_1[16] = 0f32
-    conv2d_nchw_1[20] = 0f32
-    conv2d_nchw_1[24] = 0f32
+  attr [IterVar(threadIdx.x: int32, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 8 {
+    conv2d_nchw_1: Buffer(conv2d_nchw, float32, [1], [], scope=&quot;local&quot;, align=4)[0] = 0f32
     conv2d_nchw_1[1] = 0f32
-    conv2d_nchw_1[5] = 0f32
-    conv2d_nchw_1[9] = 0f32
-    conv2d_nchw_1[13] = 0f32
-    conv2d_nchw_1[17] = 0f32
-    conv2d_nchw_1[21] = 0f32
-    conv2d_nchw_1[25] = 0f32
     conv2d_nchw_1[2] = 0f32
-    conv2d_nchw_1[6] = 0f32
-    conv2d_nchw_1[10] = 0f32
-    conv2d_nchw_1[14] = 0f32
-    conv2d_nchw_1[18] = 0f32
-    conv2d_nchw_1[22] = 0f32
-    conv2d_nchw_1[26] = 0f32
     conv2d_nchw_1[3] = 0f32
+    conv2d_nchw_1[4] = 0f32
+    conv2d_nchw_1[5] = 0f32
+    conv2d_nchw_1[6] = 0f32
     conv2d_nchw_1[7] = 0f32
+    conv2d_nchw_1[8] = 0f32
+    conv2d_nchw_1[9] = 0f32
+    conv2d_nchw_1[10] = 0f32
     conv2d_nchw_1[11] = 0f32
-    conv2d_nchw_1[15] = 0f32
-    conv2d_nchw_1[19] = 0f32
-    conv2d_nchw_1[23] = 0f32
-    conv2d_nchw_1[27] = 0f32
+    conv2d_nchw_1[12] = 0f32
+    conv2d_nchw_1[13] = 0f32
     for (rc.outer.outer: int32, 0, 64) {
       for (ry.outer.outer: int32, 0, 3) {
-        let cse_var_4: int32 = (rc.outer.outer*392)
-        let cse_var_3: int32 = (ry.outer.outer*7)
-        let cse_var_2: int32 = (rc.outer.outer*72)
+        let cse_var_3: int32 = (rc.outer.outer*392)
+        let cse_var_2: int32 = (ry.outer.outer*7)
         let cse_var_1: int32 = (ry.outer.outer*3)
          {
-          attr [IterVar(threadIdx.x_1: int32, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 28;
-          pad_temp.shared_1: Buffer(pad_temp.shared, float32, [504], [], scope=&quot;shared&quot;)[threadIdx.x_1] = @tir.if_then_else((((1 &lt;= (floordiv(threadIdx.x_1, 9) + ry.outer.outer)) &amp;&amp; (1 &lt;= floormod(threadIdx.x_1, 9))) &amp;&amp; (floormod(threadIdx.x_1, 9) &lt; 8)), data[((((cse_var_4 + (floordiv(threadIdx.x_1, 9)*7)) + cse_var_3) + floormod(threadIdx.x_1, 9)) - 8)], 0f32, dtype=float32)
-          attr [IterVar(threadIdx.x_1, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 28;
-          pad_temp.shared_1[(threadIdx.x_1 + 28)] = @tir.if_then_else(((((floordiv((threadIdx.x_1 + 28), 9) + ry.outer.outer) &lt; 8) &amp;&amp; (1 &lt;= floormod((threadIdx.x_1 + 1), 9))) &amp;&amp; (floormod((threadIdx.x_1 + 1), 9) &lt; 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 28), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 1), 9)) - 8)], 0f32, dtype=float32)
-          attr [IterVar(threadIdx.x_1, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 28;
-          pad_temp.shared_1[(threadIdx.x_1 + 56)] = @tir.if_then_else(((((1 &lt;= (floordiv(floormod((threadIdx.x_1 + 56), 63), 9) + ry.outer.outer)) &amp;&amp; ((floordiv(floormod((threadIdx.x_1 + 56), 63), 9) + ry.outer.outer) &lt; 8)) &amp;&amp; (1 &lt;= floormod((threadIdx.x_1 + 2), 9))) &amp;&amp; (floormod((threadIdx.x_1 + 2), 9) &lt; 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 56), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 2), 9)) - 8)], 0f32, dtype=float32)
-          attr [IterVar(threadIdx.x_1, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 28;
-          pad_temp.shared_1[(threadIdx.x_1 + 84)] = @tir.if_then_else(((1 &lt;= floormod((threadIdx.x_1 + 3), 9)) &amp;&amp; (floormod((threadIdx.x_1 + 3), 9) &lt; 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 84), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 3), 9)) - 8)], 0f32, dtype=float32)
-          attr [IterVar(threadIdx.x_1, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 28;
-          pad_temp.shared_1[(threadIdx.x_1 + 112)] = @tir.if_then_else(((((1 &lt;= (floordiv(floormod((threadIdx.x_1 + 112), 63), 9) + ry.outer.outer)) &amp;&amp; ((floordiv(floormod((threadIdx.x_1 + 112), 63), 9) + ry.outer.outer) &lt; 8)) &amp;&amp; (1 &lt;= floormod((threadIdx.x_1 + 4), 9))) &amp;&amp; (floormod((threadIdx.x_1 + 4), 9) &lt; 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 112), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 4), 9)) - 8)], 0f32, dtype=float32)
-          attr [IterVar(threadIdx.x_1, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 28;
-          pad_temp.shared_1[(threadIdx.x_1 + 140)] = @tir.if_then_else(((1 &lt;= floormod((threadIdx.x_1 + 5), 9)) &amp;&amp; (floormod((threadIdx.x_1 + 5), 9) &lt; 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 140), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 5), 9)) - 8)], 0f32, dtype=float32)
-          attr [IterVar(threadIdx.x_1, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 28;
-          pad_temp.shared_1[(threadIdx.x_1 + 168)] = @tir.if_then_else(((((1 &lt;= (floordiv(floormod((threadIdx.x_1 + 168), 63), 9) + ry.outer.outer)) &amp;&amp; ((floordiv(floormod((threadIdx.x_1 + 168), 63), 9) + ry.outer.outer) &lt; 8)) &amp;&amp; (1 &lt;= floormod((threadIdx.x_1 + 6), 9))) &amp;&amp; (floormod((threadIdx.x_1 + 6), 9) &lt; 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 168), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 6), 9)) - 8)], 0f32, dtype=float32)
-          attr [IterVar(threadIdx.x_1, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 28;
-          pad_temp.shared_1[(threadIdx.x_1 + 196)] = @tir.if_then_else((((1 &lt;= (floordiv(floormod((threadIdx.x_1 + 196), 63), 9) + ry.outer.outer)) &amp;&amp; (1 &lt;= floormod((threadIdx.x_1 + 7), 9))) &amp;&amp; (floormod((threadIdx.x_1 + 7), 9) &lt; 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 196), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 7), 9)) - 8)], 0f32, dtype=float32)
-          attr [IterVar(threadIdx.x_1, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 28;
-          pad_temp.shared_1[(threadIdx.x_1 + 224)] = @tir.if_then_else(((((floordiv(floormod((threadIdx.x_1 + 224), 63), 9) + ry.outer.outer) &lt; 8) &amp;&amp; (1 &lt;= floormod((threadIdx.x_1 + 8), 9))) &amp;&amp; (floormod((threadIdx.x_1 + 8), 9) &lt; 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 224), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 8), 9)) - 8)], 0f32, dtype=float32)
-          attr [IterVar(threadIdx.x_1, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 28;
-          pad_temp.shared_1[(threadIdx.x_1 + 252)] = @tir.if_then_else((((1 &lt;= (floordiv(threadIdx.x_1, 9) + ry.outer.outer)) &amp;&amp; (1 &lt;= floormod(threadIdx.x_1, 9))) &amp;&amp; (floormod(threadIdx.x_1, 9) &lt; 8)), data[((((cse_var_4 + (floordiv(threadIdx.x_1, 9)*7)) + cse_var_3) + floormod(threadIdx.x_1, 9)) + 188)], 0f32, dtype=float32)
-          attr [IterVar(threadIdx.x_1, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 28;
-          pad_temp.shared_1[(threadIdx.x_1 + 280)] = @tir.if_then_else(((((floordiv(floormod((threadIdx.x_1 + 280), 63), 9) + ry.outer.outer) &lt; 8) &amp;&amp; (1 &lt;= floormod((threadIdx.x_1 + 1), 9))) &amp;&amp; (floormod((threadIdx.x_1 + 1), 9) &lt; 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 280), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 1), 9)) - 8)], 0f32, dtype=float32)
-          attr [IterVar(threadIdx.x_1, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 28;
-          pad_temp.shared_1[(threadIdx.x_1 + 308)] = @tir.if_then_else(((((1 &lt;= (floordiv(floormod((threadIdx.x_1 + 308), 63), 9) + ry.outer.outer)) &amp;&amp; ((floordiv(floormod((threadIdx.x_1 + 308), 63), 9) + ry.outer.outer) &lt; 8)) &amp;&amp; (1 &lt;= floormod((threadIdx.x_1 + 2), 9))) &amp;&amp; (floormod((threadIdx.x_1 + 2), 9) &lt; 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 308), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 2), 9)) - 8)], 0f32, dtype=float32)
-          attr [IterVar(threadIdx.x_1, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 28;
-          pad_temp.shared_1[(threadIdx.x_1 + 336)] = @tir.if_then_else(((1 &lt;= floormod((threadIdx.x_1 + 3), 9)) &amp;&amp; (floormod((threadIdx.x_1 + 3), 9) &lt; 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 336), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 3), 9)) - 8)], 0f32, dtype=float32)
-          attr [IterVar(threadIdx.x_1, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 28;
-          pad_temp.shared_1[(threadIdx.x_1 + 364)] = @tir.if_then_else(((((1 &lt;= (floordiv(floormod((threadIdx.x_1 + 364), 63), 9) + ry.outer.outer)) &amp;&amp; ((floordiv(floormod((threadIdx.x_1 + 364), 63), 9) + ry.outer.outer) &lt; 8)) &amp;&amp; (1 &lt;= floormod((threadIdx.x_1 + 4), 9))) &amp;&amp; (floormod((threadIdx.x_1 + 4), 9) &lt; 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 364), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 4), 9)) - 8)], 0f32, dtype=float32)
-          attr [IterVar(threadIdx.x_1, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 28;
-          pad_temp.shared_1[(threadIdx.x_1 + 392)] = @tir.if_then_else(((1 &lt;= floormod((threadIdx.x_1 + 5), 9)) &amp;&amp; (floormod((threadIdx.x_1 + 5), 9) &lt; 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 392), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 5), 9)) - 8)], 0f32, dtype=float32)
-          attr [IterVar(threadIdx.x_1, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 28;
-          pad_temp.shared_1[(threadIdx.x_1 + 420)] = @tir.if_then_else(((((1 &lt;= (floordiv(floormod((threadIdx.x_1 + 420), 63), 9) + ry.outer.outer)) &amp;&amp; ((floordiv(floormod((threadIdx.x_1 + 420), 63), 9) + ry.outer.outer) &lt; 8)) &amp;&amp; (1 &lt;= floormod((threadIdx.x_1 + 6), 9))) &amp;&amp; (floormod((threadIdx.x_1 + 6), 9) &lt; 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 420), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 6), 9)) - 8)], 0f32, dtype=float32)
-          attr [IterVar(threadIdx.x_1, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 28;
-          pad_temp.shared_1[(threadIdx.x_1 + 448)] = @tir.if_then_else((((1 &lt;= (floordiv(floormod((threadIdx.x_1 + 448), 63), 9) + ry.outer.outer)) &amp;&amp; (1 &lt;= floormod((threadIdx.x_1 + 7), 9))) &amp;&amp; (floormod((threadIdx.x_1 + 7), 9) &lt; 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 448), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 7), 9)) - 8)], 0f32, dtype=float32)
-          attr [IterVar(threadIdx.x_1, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 28;
-          pad_temp.shared_1[(threadIdx.x_1 + 476)] = @tir.if_then_else(((((floordiv(floormod((threadIdx.x_1 + 476), 63), 9) + ry.outer.outer) &lt; 8) &amp;&amp; (1 &lt;= floormod((threadIdx.x_1 + 8), 9))) &amp;&amp; (floormod((threadIdx.x_1 + 8), 9) &lt; 8)), data[((((cse_var_4 + (floordiv((threadIdx.x_1 + 476), 9)*7)) + cse_var_3) + floormod((threadIdx.x_1 + 8), 9)) - 8)], 0f32, dtype=float32)
-          attr [IterVar(threadIdx.x_2: int32, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 28;
-          kernel.shared_1: Buffer(kernel.shared, float32, [384], [], scope=&quot;shared&quot;)[threadIdx.x_2] = kernel[((((((blockIdx.x*73728) + (floordiv(threadIdx.x_2, 24)*4608)) + cse_var_2) + (floordiv(floormod(threadIdx.x_2, 24), 3)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3))]
-          attr [IterVar(threadIdx.x_2, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 28;
-          kernel.shared_1[(threadIdx.x_2 + 28)] = kernel[((((((blockIdx.x*73728) + (floordiv((floordiv(threadIdx.x_2, 4) + 7), 6)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 28), 24), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3))]
-          attr [IterVar(threadIdx.x_2, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 28;
-          kernel.shared_1[(threadIdx.x_2 + 56)] = kernel[((((((blockIdx.x*73728) + (floordiv((floordiv(threadIdx.x_2, 4) + 14), 6)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 56), 24), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3))]
-          attr [IterVar(threadIdx.x_2, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 28;
-          kernel.shared_1[(threadIdx.x_2 + 84)] = kernel[((((((blockIdx.x*73728) + (floordiv((floordiv(threadIdx.x_2, 4) + 21), 6)*4608)) + cse_var_2) + (floormod((floordiv(threadIdx.x_2, 3) + 4), 8)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3))]
-          attr [IterVar(threadIdx.x_2, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 28;
-          kernel.shared_1[(threadIdx.x_2 + 112)] = kernel[((((((blockIdx.x*73728) + (floordiv((floordiv(threadIdx.x_2, 4) + 28), 6)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 112), 24), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3))]
-          attr [IterVar(threadIdx.x_2, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 28;
-          kernel.shared_1[(threadIdx.x_2 + 140)] = kernel[((((((blockIdx.x*73728) + (floordiv((floordiv(threadIdx.x_2, 4) + 35), 6)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 140), 24), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3))]
-          attr [IterVar(threadIdx.x_2, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 28;
-          kernel.shared_1[(threadIdx.x_2 + 168)] = kernel[(((((((blockIdx.x*73728) + (floordiv(floordiv(threadIdx.x_2, 4), 6)*4608)) + cse_var_2) + (floordiv(floormod(threadIdx.x_2, 24), 3)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3)) + 32256)]
-          attr [IterVar(threadIdx.x_2, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 28;
-          kernel.shared_1[(threadIdx.x_2 + 196)] = kernel[((((((blockIdx.x*73728) + (floordiv((floordiv(threadIdx.x_2, 4) + 49), 6)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 196), 24), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3))]
-          attr [IterVar(threadIdx.x_2, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 28;
-          kernel.shared_1[(threadIdx.x_2 + 224)] = kernel[((((((blockIdx.x*73728) + (floordiv((floordiv(threadIdx.x_2, 4) + 56), 6)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 224), 24), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3))]
-          attr [IterVar(threadIdx.x_2, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 28;
-          kernel.shared_1[(threadIdx.x_2 + 252)] = kernel[((((((blockIdx.x*73728) + (floordiv((floordiv(threadIdx.x_2, 4) + 63), 6)*4608)) + cse_var_2) + (floormod((floordiv(threadIdx.x_2, 3) + 4), 8)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3))]
-          attr [IterVar(threadIdx.x_2, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 28;
-          kernel.shared_1[(threadIdx.x_2 + 280)] = kernel[((((((blockIdx.x*73728) + (floordiv((floordiv(threadIdx.x_2, 4) + 70), 6)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 280), 24), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3))]
-          attr [IterVar(threadIdx.x_2, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 28;
-          kernel.shared_1[(threadIdx.x_2 + 308)] = kernel[((((((blockIdx.x*73728) + (floordiv((floordiv(threadIdx.x_2, 4) + 77), 6)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 308), 24), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3))]
-          attr [IterVar(threadIdx.x_2, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 28;
-          kernel.shared_1[(threadIdx.x_2 + 336)] = kernel[(((((((blockIdx.x*73728) + (floordiv(floordiv(threadIdx.x_2, 4), 6)*4608)) + cse_var_2) + (floordiv(floormod(threadIdx.x_2, 24), 3)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3)) + 64512)]
-          attr [IterVar(threadIdx.x_2, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 28;
-          if @tir.likely((threadIdx.x_2 &lt; 20), dtype=bool) {
-            kernel.shared_1[(threadIdx.x_2 + 364)] = kernel[((((((blockIdx.x*73728) + (floordiv((floordiv(threadIdx.x_2, 4) + 91), 6)*4608)) + cse_var_2) + (floordiv(floormod((threadIdx.x_2 + 364), 24), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3))]
-          }
-          for (rx.outer.inner: int32, 0, 3) {
-            conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[(rx.outer.inner + floormod(threadIdx.x, 7))]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + rx.outer.inner)]))
-            conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 9)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + rx.outer.inner)]))
-            conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 18)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + rx.outer.inner)]))
-            conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 27)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + rx.outer.inner)]))
-            conv2d_nchw_1[16] = (conv2d_nchw_1[16] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 36)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + rx.outer.inner)]))
-            conv2d_nchw_1[20] = (conv2d_nchw_1[20] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 45)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + rx.outer.inner)]))
-            conv2d_nchw_1[24] = (conv2d_nchw_1[24] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 54)]*kernel.shared_1[((floordiv(threadIdx.x, 7)*96) + rx.outer.inner)]))
-            conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 63)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 3)]))
-            conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 72)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 3)]))
-            conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 81)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 3)]))
-            conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 90)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 3)]))
-            conv2d_nchw_1[16] = (conv2d_nchw_1[16] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 99)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 3)]))
-            conv2d_nchw_1[20] = (conv2d_nchw_1[20] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 108)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 3)]))
-            conv2d_nchw_1[24] = (conv2d_nchw_1[24] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 117)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 3)]))
-            conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 126)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 6)]))
-            conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 135)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 6)]))
-            conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 144)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 6)]))
-            conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 153)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 6)]))
-            conv2d_nchw_1[16] = (conv2d_nchw_1[16] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 162)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 6)]))
-            conv2d_nchw_1[20] = (conv2d_nchw_1[20] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 171)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 6)]))
-            conv2d_nchw_1[24] = (conv2d_nchw_1[24] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 180)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 6)]))
-            conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 189)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 9)]))
-            conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 198)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 9)]))
-            conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 207)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 9)]))
-            conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 216)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 9)]))
-            conv2d_nchw_1[16] = (conv2d_nchw_1[16] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 225)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 9)]))
-            conv2d_nchw_1[20] = (conv2d_nchw_1[20] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 234)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 9)]))
-            conv2d_nchw_1[24] = (conv2d_nchw_1[24] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 243)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 9)]))
-            conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 252)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 12)]))
-            conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 261)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 12)]))
-            conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 270)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 12)]))
-            conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 279)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 12)]))
-            conv2d_nchw_1[16] = (conv2d_nchw_1[16] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 288)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 12)]))
-            conv2d_nchw_1[20] = (conv2d_nchw_1[20] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 297)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 12)]))
-            conv2d_nchw_1[24] = (conv2d_nchw_1[24] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 306)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 12)]))
-            conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 315)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 15)]))
-            conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 324)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 15)]))
-            conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 333)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 15)]))
-            conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 342)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 15)]))
-            conv2d_nchw_1[16] = (conv2d_nchw_1[16] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 351)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 15)]))
-            conv2d_nchw_1[20] = (conv2d_nchw_1[20] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 360)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 15)]))
-            conv2d_nchw_1[24] = (conv2d_nchw_1[24] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 369)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 15)]))
-            conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 378)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 18)]))
-            conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 387)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 18)]))
-            conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 396)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 18)]))
-            conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 405)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 18)]))
-            conv2d_nchw_1[16] = (conv2d_nchw_1[16] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 414)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 18)]))
-            conv2d_nchw_1[20] = (conv2d_nchw_1[20] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 423)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 18)]))
-            conv2d_nchw_1[24] = (conv2d_nchw_1[24] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 432)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 18)]))
-            conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 441)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 21)]))
-            conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 450)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 21)]))
-            conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 459)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 21)]))
-            conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 468)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 21)]))
-            conv2d_nchw_1[16] = (conv2d_nchw_1[16] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 477)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 21)]))
-            conv2d_nchw_1[20] = (conv2d_nchw_1[20] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 486)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 21)]))
-            conv2d_nchw_1[24] = (conv2d_nchw_1[24] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 495)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 21)]))
-            conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[(rx.outer.inner + floormod(threadIdx.x, 7))]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 24)]))
-            conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 9)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 24)]))
-            conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 18)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 24)]))
-            conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 27)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 24)]))
-            conv2d_nchw_1[17] = (conv2d_nchw_1[17] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 36)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 24)]))
-            conv2d_nchw_1[21] = (conv2d_nchw_1[21] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 45)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 24)]))
-            conv2d_nchw_1[25] = (conv2d_nchw_1[25] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 54)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 24)]))
-            conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 63)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 27)]))
-            conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 72)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 27)]))
-            conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 81)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 27)]))
-            conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 90)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 27)]))
-            conv2d_nchw_1[17] = (conv2d_nchw_1[17] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 99)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 27)]))
-            conv2d_nchw_1[21] = (conv2d_nchw_1[21] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 108)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 27)]))
-            conv2d_nchw_1[25] = (conv2d_nchw_1[25] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 117)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 27)]))
-            conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 126)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 30)]))
-            conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 135)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 30)]))
-            conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 144)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 30)]))
-            conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 153)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 30)]))
-            conv2d_nchw_1[17] = (conv2d_nchw_1[17] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 162)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 30)]))
-            conv2d_nchw_1[21] = (conv2d_nchw_1[21] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 171)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 30)]))
-            conv2d_nchw_1[25] = (conv2d_nchw_1[25] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 180)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 30)]))
-            conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 189)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 33)]))
-            conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 198)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 33)]))
-            conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 207)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 33)]))
-            conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 216)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 33)]))
-            conv2d_nchw_1[17] = (conv2d_nchw_1[17] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 225)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 33)]))
-            conv2d_nchw_1[21] = (conv2d_nchw_1[21] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 234)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 33)]))
-            conv2d_nchw_1[25] = (conv2d_nchw_1[25] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 243)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 33)]))
-            conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 252)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 36)]))
-            conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 261)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 36)]))
-            conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 270)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 36)]))
-            conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 279)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 36)]))
-            conv2d_nchw_1[17] = (conv2d_nchw_1[17] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 288)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 36)]))
-            conv2d_nchw_1[21] = (conv2d_nchw_1[21] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 297)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 36)]))
-            conv2d_nchw_1[25] = (conv2d_nchw_1[25] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 306)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 36)]))
-            conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 315)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 39)]))
-            conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 324)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 39)]))
-            conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 333)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 39)]))
-            conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 342)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 39)]))
-            conv2d_nchw_1[17] = (conv2d_nchw_1[17] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 351)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 39)]))
-            conv2d_nchw_1[21] = (conv2d_nchw_1[21] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 360)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 39)]))
-            conv2d_nchw_1[25] = (conv2d_nchw_1[25] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 369)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 39)]))
-            conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 378)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 42)]))
-            conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 387)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 42)]))
-            conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 396)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 42)]))
-            conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 405)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 42)]))
-            conv2d_nchw_1[17] = (conv2d_nchw_1[17] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 414)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 42)]))
-            conv2d_nchw_1[21] = (conv2d_nchw_1[21] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 423)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 42)]))
-            conv2d_nchw_1[25] = (conv2d_nchw_1[25] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 432)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 42)]))
-            conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 441)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 45)]))
-            conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 450)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 45)]))
-            conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 459)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 45)]))
-            conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 468)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 45)]))
-            conv2d_nchw_1[17] = (conv2d_nchw_1[17] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 477)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 45)]))
-            conv2d_nchw_1[21] = (conv2d_nchw_1[21] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 486)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 45)]))
-            conv2d_nchw_1[25] = (conv2d_nchw_1[25] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 495)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 45)]))
-            conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[(rx.outer.inner + floormod(threadIdx.x, 7))]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 48)]))
-            conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 9)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 48)]))
-            conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 18)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 48)]))
-            conv2d_nchw_1[14] = (conv2d_nchw_1[14] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 27)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 48)]))
-            conv2d_nchw_1[18] = (conv2d_nchw_1[18] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 36)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 48)]))
-            conv2d_nchw_1[22] = (conv2d_nchw_1[22] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 45)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 48)]))
-            conv2d_nchw_1[26] = (conv2d_nchw_1[26] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 54)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 48)]))
-            conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 63)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 51)]))
-            conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 72)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 51)]))
-            conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 81)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 51)]))
-            conv2d_nchw_1[14] = (conv2d_nchw_1[14] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 90)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 51)]))
-            conv2d_nchw_1[18] = (conv2d_nchw_1[18] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 99)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 51)]))
-            conv2d_nchw_1[22] = (conv2d_nchw_1[22] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 108)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 51)]))
-            conv2d_nchw_1[26] = (conv2d_nchw_1[26] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 117)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 51)]))
-            conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 126)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 54)]))
-            conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 135)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 54)]))
-            conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 144)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 54)]))
-            conv2d_nchw_1[14] = (conv2d_nchw_1[14] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 153)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 54)]))
-            conv2d_nchw_1[18] = (conv2d_nchw_1[18] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 162)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 54)]))
-            conv2d_nchw_1[22] = (conv2d_nchw_1[22] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 171)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 54)]))
-            conv2d_nchw_1[26] = (conv2d_nchw_1[26] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 180)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 54)]))
-            conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 189)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 57)]))
-            conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 198)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 57)]))
-            conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 207)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 57)]))
-            conv2d_nchw_1[14] = (conv2d_nchw_1[14] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 216)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 57)]))
-            conv2d_nchw_1[18] = (conv2d_nchw_1[18] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 225)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 57)]))
-            conv2d_nchw_1[22] = (conv2d_nchw_1[22] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 234)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 57)]))
-            conv2d_nchw_1[26] = (conv2d_nchw_1[26] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 243)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 57)]))
-            conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 252)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 60)]))
-            conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 261)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 60)]))
-            conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 270)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 60)]))
-            conv2d_nchw_1[14] = (conv2d_nchw_1[14] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 279)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 60)]))
-            conv2d_nchw_1[18] = (conv2d_nchw_1[18] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 288)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 60)]))
-            conv2d_nchw_1[22] = (conv2d_nchw_1[22] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 297)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 60)]))
-            conv2d_nchw_1[26] = (conv2d_nchw_1[26] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 306)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 60)]))
-            conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 315)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 63)]))
-            conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 324)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 63)]))
-            conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 333)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 63)]))
-            conv2d_nchw_1[14] = (conv2d_nchw_1[14] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 342)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 63)]))
-            conv2d_nchw_1[18] = (conv2d_nchw_1[18] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 351)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 63)]))
-            conv2d_nchw_1[22] = (conv2d_nchw_1[22] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 360)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 63)]))
-            conv2d_nchw_1[26] = (conv2d_nchw_1[26] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 369)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 63)]))
-            conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 378)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 66)]))
-            conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 387)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 66)]))
-            conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 396)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 66)]))
-            conv2d_nchw_1[14] = (conv2d_nchw_1[14] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 405)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 66)]))
-            conv2d_nchw_1[18] = (conv2d_nchw_1[18] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 414)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 66)]))
-            conv2d_nchw_1[22] = (conv2d_nchw_1[22] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 423)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 66)]))
-            conv2d_nchw_1[26] = (conv2d_nchw_1[26] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 432)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 66)]))
-            conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 441)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 69)]))
-            conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 450)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 69)]))
-            conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 459)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 69)]))
-            conv2d_nchw_1[14] = (conv2d_nchw_1[14] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 468)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 69)]))
-            conv2d_nchw_1[18] = (conv2d_nchw_1[18] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 477)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 69)]))
-            conv2d_nchw_1[22] = (conv2d_nchw_1[22] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 486)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 69)]))
-            conv2d_nchw_1[26] = (conv2d_nchw_1[26] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 495)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 69)]))
-            conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[(rx.outer.inner + floormod(threadIdx.x, 7))]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 72)]))
-            conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 9)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 72)]))
-            conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 18)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 72)]))
-            conv2d_nchw_1[15] = (conv2d_nchw_1[15] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 27)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 72)]))
-            conv2d_nchw_1[19] = (conv2d_nchw_1[19] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 36)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 72)]))
-            conv2d_nchw_1[23] = (conv2d_nchw_1[23] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 45)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 72)]))
-            conv2d_nchw_1[27] = (conv2d_nchw_1[27] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 54)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 72)]))
-            conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 63)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 75)]))
-            conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 72)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 75)]))
-            conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 81)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 75)]))
-            conv2d_nchw_1[15] = (conv2d_nchw_1[15] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 90)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 75)]))
-            conv2d_nchw_1[19] = (conv2d_nchw_1[19] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 99)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 75)]))
-            conv2d_nchw_1[23] = (conv2d_nchw_1[23] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 108)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 75)]))
-            conv2d_nchw_1[27] = (conv2d_nchw_1[27] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 117)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 75)]))
-            conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 126)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 78)]))
-            conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 135)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 78)]))
-            conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 144)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 78)]))
-            conv2d_nchw_1[15] = (conv2d_nchw_1[15] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 153)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 78)]))
-            conv2d_nchw_1[19] = (conv2d_nchw_1[19] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 162)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 78)]))
-            conv2d_nchw_1[23] = (conv2d_nchw_1[23] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 171)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 78)]))
-            conv2d_nchw_1[27] = (conv2d_nchw_1[27] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 180)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 78)]))
-            conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 189)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 81)]))
-            conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 198)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 81)]))
-            conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 207)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 81)]))
-            conv2d_nchw_1[15] = (conv2d_nchw_1[15] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 216)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 81)]))
-            conv2d_nchw_1[19] = (conv2d_nchw_1[19] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 225)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 81)]))
-            conv2d_nchw_1[23] = (conv2d_nchw_1[23] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 234)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 81)]))
-            conv2d_nchw_1[27] = (conv2d_nchw_1[27] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 243)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 81)]))
-            conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 252)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 84)]))
-            conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 261)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 84)]))
-            conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 270)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 84)]))
-            conv2d_nchw_1[15] = (conv2d_nchw_1[15] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 279)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 84)]))
-            conv2d_nchw_1[19] = (conv2d_nchw_1[19] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 288)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 84)]))
-            conv2d_nchw_1[23] = (conv2d_nchw_1[23] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 297)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 84)]))
-            conv2d_nchw_1[27] = (conv2d_nchw_1[27] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 306)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 84)]))
-            conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 315)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 87)]))
-            conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 324)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 87)]))
-            conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 333)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 87)]))
-            conv2d_nchw_1[15] = (conv2d_nchw_1[15] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 342)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 87)]))
-            conv2d_nchw_1[19] = (conv2d_nchw_1[19] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 351)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 87)]))
-            conv2d_nchw_1[23] = (conv2d_nchw_1[23] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 360)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 87)]))
-            conv2d_nchw_1[27] = (conv2d_nchw_1[27] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 369)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 87)]))
-            conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 378)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 90)]))
-            conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 387)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 90)]))
-            conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 396)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 90)]))
-            conv2d_nchw_1[15] = (conv2d_nchw_1[15] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 405)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 90)]))
-            conv2d_nchw_1[19] = (conv2d_nchw_1[19] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 414)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 90)]))
-            conv2d_nchw_1[23] = (conv2d_nchw_1[23] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 423)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 90)]))
-            conv2d_nchw_1[27] = (conv2d_nchw_1[27] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 432)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 90)]))
-            conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 441)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 93)]))
-            conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 450)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 93)]))
-            conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 459)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 93)]))
-            conv2d_nchw_1[15] = (conv2d_nchw_1[15] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 468)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 93)]))
-            conv2d_nchw_1[19] = (conv2d_nchw_1[19] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 477)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 93)]))
-            conv2d_nchw_1[23] = (conv2d_nchw_1[23] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 486)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 93)]))
-            conv2d_nchw_1[27] = (conv2d_nchw_1[27] + (pad_temp.shared_1[((rx.outer.inner + floormod(threadIdx.x, 7)) + 495)]*kernel.shared_1[(((floordiv(threadIdx.x, 7)*96) + rx.outer.inner) + 93)]))
-          }
+          attr [IterVar(threadIdx.x_1: int32, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 8;
+          pad_temp.shared_1: Buffer(pad_temp.shared, float32, [72], [], scope=&quot;shared&quot;)[threadIdx.x_1] = @tir.if_then_else((((1 &lt;= (ry.outer.outer + floormod(blockIdx.x, 7))) &amp;&amp; ((ry.outer.outer + floormod(blockIdx.x, 7)) &lt; 8)) &amp;&amp; (1 &lt;= threadIdx.x_1)), data[((((cse_var_3 + cse_var_2) + (floormod(blockIdx.x, 7)*7)) + threadIdx.x_1) - 8)], 0f32, dtype=float32)
+          attr [IterVar(threadIdx.x_1, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 8;
+          pad_temp.shared_1[(threadIdx.x_1 + 8)] = @tir.if_then_else(((((1 &lt;= (ry.outer.outer + floormod(blockIdx.x, 7))) &amp;&amp; ((ry.outer.outer + floormod(blockIdx.x, 7)) &lt; 8)) &amp;&amp; (1 &lt;= floormod((threadIdx.x_1 + 8), 9))) &amp;&amp; (floormod((threadIdx.x_1 + 8), 9) &lt; 8)), data[(((((cse_var_3 + (floordiv((threadIdx.x_1 + 8), 9)*49)) + cse_var_2) + (floormod(blockIdx.x, 7)*7)) + floormod((threadIdx.x_1 + 8), 9)) - 8)], 0f32, dtype=float32)
+          attr [IterVar(threadIdx.x_1, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 8;
+          pad_temp.shared_1[(threadIdx.x_1 + 16)] = @tir.if_then_else(((((1 &lt;= (ry.outer.outer + floormod(blockIdx.x, 7))) &amp;&amp; ((ry.outer.outer + floormod(blockIdx.x, 7)) &lt; 8)) &amp;&amp; (1 &lt;= floormod((threadIdx.x_1 + 7), 9))) &amp;&amp; (floormod((threadIdx.x_1 + 7), 9) &lt; 8)), data[(((((cse_var_3 + (floordiv((threadIdx.x_1 + 16), 9)*49)) + cse_var_2) + (floormod(blockIdx.x, 7)*7)) + floormod((threadIdx.x_1 + 7), 9)) - 8)], 0f32, dtype=float32)
+          attr [IterVar(threadIdx.x_1, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 8;
+          pad_temp.shared_1[(threadIdx.x_1 + 24)] = @tir.if_then_else(((((1 &lt;= (ry.outer.outer + floormod(blockIdx.x, 7))) &amp;&amp; ((ry.outer.outer + floormod(blockIdx.x, 7)) &lt; 8)) &amp;&amp; (1 &lt;= floormod((threadIdx.x_1 + 6), 9))) &amp;&amp; (floormod((threadIdx.x_1 + 6), 9) &lt; 8)), data[(((((cse_var_3 + (floordiv((threadIdx.x_1 + 24), 9)*49)) + cse_var_2) + (floormod(blockIdx.x, 7)*7)) + floormod((threadIdx.x_1 + 6), 9)) - 8)], 0f32, dtype=float32)
+          attr [IterVar(threadIdx.x_1, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 8;
+          pad_temp.shared_1[(threadIdx.x_1 + 32)] = @tir.if_then_else(((((1 &lt;= (ry.outer.outer + floormod(blockIdx.x, 7))) &amp;&amp; ((ry.outer.outer + floormod(blockIdx.x, 7)) &lt; 8)) &amp;&amp; (1 &lt;= floormod((threadIdx.x_1 + 5), 9))) &amp;&amp; (floormod((threadIdx.x_1 + 5), 9) &lt; 8)), data[(((((cse_var_3 + (floordiv((threadIdx.x_1 + 32), 9)*49)) + cse_var_2) + (floormod(blockIdx.x, 7)*7)) + floormod((threadIdx.x_1 + 5), 9)) - 8)], 0f32, dtype=float32)
+          attr [IterVar(threadIdx.x_1, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 8;
+          pad_temp.shared_1[(threadIdx.x_1 + 40)] = @tir.if_then_else(((((1 &lt;= (ry.outer.outer + floormod(blockIdx.x, 7))) &amp;&amp; ((ry.outer.outer + floormod(blockIdx.x, 7)) &lt; 8)) &amp;&amp; (1 &lt;= floormod((threadIdx.x_1 + 4), 9))) &amp;&amp; (floormod((threadIdx.x_1 + 4), 9) &lt; 8)), data[(((((cse_var_3 + (floordiv((threadIdx.x_1 + 40), 9)*49)) + cse_var_2) + (floormod(blockIdx.x, 7)*7)) + floormod((threadIdx.x_1 + 4), 9)) - 8)], 0f32, dtype=float32)
+          attr [IterVar(threadIdx.x_1, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 8;
+          pad_temp.shared_1[(threadIdx.x_1 + 48)] = @tir.if_then_else(((((1 &lt;= (ry.outer.outer + floormod(blockIdx.x, 7))) &amp;&amp; ((ry.outer.outer + floormod(blockIdx.x, 7)) &lt; 8)) &amp;&amp; (1 &lt;= floormod((threadIdx.x_1 + 3), 9))) &amp;&amp; (floormod((threadIdx.x_1 + 3), 9) &lt; 8)), data[(((((cse_var_3 + (floordiv((threadIdx.x_1 + 48), 9)*49)) + cse_var_2) + (floormod(blockIdx.x, 7)*7)) + floormod((threadIdx.x_1 + 3), 9)) - 8)], 0f32, dtype=float32)
+          attr [IterVar(threadIdx.x_1, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 8;
+          pad_temp.shared_1[(threadIdx.x_1 + 56)] = @tir.if_then_else(((((1 &lt;= (ry.outer.outer + floormod(blockIdx.x, 7))) &amp;&amp; ((ry.outer.outer + floormod(blockIdx.x, 7)) &lt; 8)) &amp;&amp; (1 &lt;= floormod((threadIdx.x_1 + 2), 9))) &amp;&amp; (floormod((threadIdx.x_1 + 2), 9) &lt; 8)), data[(((((cse_var_3 + (floordiv((threadIdx.x_1 + 56), 9)*49)) + cse_var_2) + (floormod(blockIdx.x, 7)*7)) + floormod((threadIdx.x_1 + 2), 9)) - 8)], 0f32, dtype=float32)
+          attr [IterVar(threadIdx.x_1, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 8;
+          pad_temp.shared_1[(threadIdx.x_1 + 64)] = @tir.if_then_else((((1 &lt;= (ry.outer.outer + floormod(blockIdx.x, 7))) &amp;&amp; ((ry.outer.outer + floormod(blockIdx.x, 7)) &lt; 8)) &amp;&amp; (floormod((threadIdx.x_1 + 1), 9) &lt; 8)), data[(((((cse_var_3 + (floordiv((threadIdx.x_1 + 64), 9)*49)) + cse_var_2) + (floormod(blockIdx.x, 7)*7)) + floormod((threadIdx.x_1 + 1), 9)) - 8)], 0f32, dtype=float32)
+          attr [IterVar(threadIdx.x_2: int32, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 8;
+          kernel.shared_1: Buffer(kernel.shared, float32, [384], [], scope=&quot;shared&quot;)[threadIdx.x_2] = kernel[(((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv(threadIdx.x_2, 3)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3))]
+          attr [IterVar(threadIdx.x_2, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 8;
+          kernel.shared_1[(threadIdx.x_2 + 8)] = kernel[(((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv((threadIdx.x_2 + 8), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3))]
+          attr [IterVar(threadIdx.x_2, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 8;
+          kernel.shared_1[(threadIdx.x_2 + 16)] = kernel[(((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv((threadIdx.x_2 + 16), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3))]
+          attr [IterVar(threadIdx.x_2, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 8;
+          kernel.shared_1[(threadIdx.x_2 + 24)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv(threadIdx.x_2, 3)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3)) + 4608)]
+          attr [IterVar(threadIdx.x_2, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 8;
+          kernel.shared_1[(threadIdx.x_2 + 32)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv((threadIdx.x_2 + 8), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3)) + 4608)]
+          attr [IterVar(threadIdx.x_2, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 8;
+          kernel.shared_1[(threadIdx.x_2 + 40)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv((threadIdx.x_2 + 16), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3)) + 4608)]
+          attr [IterVar(threadIdx.x_2, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 8;
+          kernel.shared_1[(threadIdx.x_2 + 48)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv(threadIdx.x_2, 3)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3)) + 9216)]
+          attr [IterVar(threadIdx.x_2, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 8;
+          kernel.shared_1[(threadIdx.x_2 + 56)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv((threadIdx.x_2 + 8), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3)) + 9216)]
+          attr [IterVar(threadIdx.x_2, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 8;
+          kernel.shared_1[(threadIdx.x_2 + 64)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv((threadIdx.x_2 + 16), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3)) + 9216)]
+          attr [IterVar(threadIdx.x_2, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 8;
+          kernel.shared_1[(threadIdx.x_2 + 72)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv(threadIdx.x_2, 3)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3)) + 13824)]
+          attr [IterVar(threadIdx.x_2, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 8;
+          kernel.shared_1[(threadIdx.x_2 + 80)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv((threadIdx.x_2 + 8), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3)) + 13824)]
+          attr [IterVar(threadIdx.x_2, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 8;
+          kernel.shared_1[(threadIdx.x_2 + 88)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv((threadIdx.x_2 + 16), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3)) + 13824)]
+          attr [IterVar(threadIdx.x_2, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 8;
+          kernel.shared_1[(threadIdx.x_2 + 96)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv(threadIdx.x_2, 3)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3)) + 18432)]
+          attr [IterVar(threadIdx.x_2, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 8;
+          kernel.shared_1[(threadIdx.x_2 + 104)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv((threadIdx.x_2 + 8), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3)) + 18432)]
+          attr [IterVar(threadIdx.x_2, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 8;
+          kernel.shared_1[(threadIdx.x_2 + 112)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv((threadIdx.x_2 + 16), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3)) + 18432)]
+          attr [IterVar(threadIdx.x_2, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 8;
+          kernel.shared_1[(threadIdx.x_2 + 120)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv(threadIdx.x_2, 3)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3)) + 23040)]
+          attr [IterVar(threadIdx.x_2, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 8;
+          kernel.shared_1[(threadIdx.x_2 + 128)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv((threadIdx.x_2 + 8), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3)) + 23040)]
+          attr [IterVar(threadIdx.x_2, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 8;
+          kernel.shared_1[(threadIdx.x_2 + 136)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv((threadIdx.x_2 + 16), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3)) + 23040)]
+          attr [IterVar(threadIdx.x_2, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 8;
+          kernel.shared_1[(threadIdx.x_2 + 144)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv(threadIdx.x_2, 3)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3)) + 27648)]
+          attr [IterVar(threadIdx.x_2, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 8;
+          kernel.shared_1[(threadIdx.x_2 + 152)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv((threadIdx.x_2 + 8), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3)) + 27648)]
+          attr [IterVar(threadIdx.x_2, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 8;
+          kernel.shared_1[(threadIdx.x_2 + 160)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv((threadIdx.x_2 + 16), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3)) + 27648)]
+          attr [IterVar(threadIdx.x_2, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 8;
+          kernel.shared_1[(threadIdx.x_2 + 168)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv(threadIdx.x_2, 3)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3)) + 32256)]
+          attr [IterVar(threadIdx.x_2, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 8;
+          kernel.shared_1[(threadIdx.x_2 + 176)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv((threadIdx.x_2 + 8), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3)) + 32256)]
+          attr [IterVar(threadIdx.x_2, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 8;
+          kernel.shared_1[(threadIdx.x_2 + 184)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv((threadIdx.x_2 + 16), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3)) + 32256)]
+          attr [IterVar(threadIdx.x_2, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 8;
+          kernel.shared_1[(threadIdx.x_2 + 192)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv(threadIdx.x_2, 3)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3)) + 36864)]
+          attr [IterVar(threadIdx.x_2, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 8;
+          kernel.shared_1[(threadIdx.x_2 + 200)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv((threadIdx.x_2 + 8), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3)) + 36864)]
+          attr [IterVar(threadIdx.x_2, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 8;
+          kernel.shared_1[(threadIdx.x_2 + 208)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv((threadIdx.x_2 + 16), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3)) + 36864)]
+          attr [IterVar(threadIdx.x_2, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 8;
+          kernel.shared_1[(threadIdx.x_2 + 216)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv(threadIdx.x_2, 3)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3)) + 41472)]
+          attr [IterVar(threadIdx.x_2, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 8;
+          kernel.shared_1[(threadIdx.x_2 + 224)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv((threadIdx.x_2 + 8), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3)) + 41472)]
+          attr [IterVar(threadIdx.x_2, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 8;
+          kernel.shared_1[(threadIdx.x_2 + 232)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv((threadIdx.x_2 + 16), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3)) + 41472)]
+          attr [IterVar(threadIdx.x_2, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 8;
+          kernel.shared_1[(threadIdx.x_2 + 240)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv(threadIdx.x_2, 3)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3)) + 46080)]
+          attr [IterVar(threadIdx.x_2, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 8;
+          kernel.shared_1[(threadIdx.x_2 + 248)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv((threadIdx.x_2 + 8), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3)) + 46080)]
+          attr [IterVar(threadIdx.x_2, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 8;
+          kernel.shared_1[(threadIdx.x_2 + 256)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv((threadIdx.x_2 + 16), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3)) + 46080)]
+          attr [IterVar(threadIdx.x_2, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 8;
+          kernel.shared_1[(threadIdx.x_2 + 264)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv(threadIdx.x_2, 3)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3)) + 50688)]
+          attr [IterVar(threadIdx.x_2, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 8;
+          kernel.shared_1[(threadIdx.x_2 + 272)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv((threadIdx.x_2 + 8), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3)) + 50688)]
+          attr [IterVar(threadIdx.x_2, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 8;
+          kernel.shared_1[(threadIdx.x_2 + 280)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv((threadIdx.x_2 + 16), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3)) + 50688)]
+          attr [IterVar(threadIdx.x_2, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 8;
+          kernel.shared_1[(threadIdx.x_2 + 288)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv(threadIdx.x_2, 3)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3)) + 55296)]
+          attr [IterVar(threadIdx.x_2, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 8;
+          kernel.shared_1[(threadIdx.x_2 + 296)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv((threadIdx.x_2 + 8), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3)) + 55296)]
+          attr [IterVar(threadIdx.x_2, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 8;
+          kernel.shared_1[(threadIdx.x_2 + 304)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv((threadIdx.x_2 + 16), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3)) + 55296)]
+          attr [IterVar(threadIdx.x_2, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 8;
+          kernel.shared_1[(threadIdx.x_2 + 312)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv(threadIdx.x_2, 3)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3)) + 59904)]
+          attr [IterVar(threadIdx.x_2, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 8;
+          kernel.shared_1[(threadIdx.x_2 + 320)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv((threadIdx.x_2 + 8), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3)) + 59904)]
+          attr [IterVar(threadIdx.x_2, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 8;
+          kernel.shared_1[(threadIdx.x_2 + 328)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv((threadIdx.x_2 + 16), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3)) + 59904)]
+          attr [IterVar(threadIdx.x_2, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 8;
+          kernel.shared_1[(threadIdx.x_2 + 336)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv(threadIdx.x_2, 3)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3)) + 64512)]
+          attr [IterVar(threadIdx.x_2, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 8;
+          kernel.shared_1[(threadIdx.x_2 + 344)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv((threadIdx.x_2 + 8), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3)) + 64512)]
+          attr [IterVar(threadIdx.x_2, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 8;
+          kernel.shared_1[(threadIdx.x_2 + 352)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv((threadIdx.x_2 + 16), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3)) + 64512)]
+          attr [IterVar(threadIdx.x_2, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 8;
+          kernel.shared_1[(threadIdx.x_2 + 360)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv(threadIdx.x_2, 3)*9)) + cse_var_1) + floormod(threadIdx.x_2, 3)) + 69120)]
+          attr [IterVar(threadIdx.x_2, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 8;
+          kernel.shared_1[(threadIdx.x_2 + 368)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv((threadIdx.x_2 + 8), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 2), 3)) + 69120)]
+          attr [IterVar(threadIdx.x_2, (nullptr), &quot;ThreadIndex&quot;, &quot;threadIdx.x&quot;)] &quot;thread_extent&quot; = 8;
+          kernel.shared_1[(threadIdx.x_2 + 376)] = kernel[((((((floordiv(blockIdx.x, 7)*73728) + (rc.outer.outer*72)) + (floordiv((threadIdx.x_2 + 16), 3)*9)) + cse_var_1) + floormod((threadIdx.x_2 + 1), 3)) + 69120)]
+          conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[0]*kernel.shared_1[(threadIdx.x*24)]))
+          conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[1]*kernel.shared_1[(threadIdx.x*24)]))
+          conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[2]*kernel.shared_1[(threadIdx.x*24)]))
+          conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[3]*kernel.shared_1[(threadIdx.x*24)]))
+          conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[4]*kernel.shared_1[(threadIdx.x*24)]))
+          conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[5]*kernel.shared_1[(threadIdx.x*24)]))
+          conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[6]*kernel.shared_1[(threadIdx.x*24)]))
+          conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[0]*kernel.shared_1[((threadIdx.x*24) + 192)]))
+          conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[1]*kernel.shared_1[((threadIdx.x*24) + 192)]))
+          conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[2]*kernel.shared_1[((threadIdx.x*24) + 192)]))
+          conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[3]*kernel.shared_1[((threadIdx.x*24) + 192)]))
+          conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[4]*kernel.shared_1[((threadIdx.x*24) + 192)]))
+          conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[5]*kernel.shared_1[((threadIdx.x*24) + 192)]))
+          conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[6]*kernel.shared_1[((threadIdx.x*24) + 192)]))
+          conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[1]*kernel.shared_1[((threadIdx.x*24) + 1)]))
+          conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[2]*kernel.shared_1[((threadIdx.x*24) + 1)]))
+          conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[3]*kernel.shared_1[((threadIdx.x*24) + 1)]))
+          conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[4]*kernel.shared_1[((threadIdx.x*24) + 1)]))
+          conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[5]*kernel.shared_1[((threadIdx.x*24) + 1)]))
+          conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[6]*kernel.shared_1[((threadIdx.x*24) + 1)]))
+          conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[7]*kernel.shared_1[((threadIdx.x*24) + 1)]))
+          conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[1]*kernel.shared_1[((threadIdx.x*24) + 193)]))
+          conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[2]*kernel.shared_1[((threadIdx.x*24) + 193)]))
+          conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[3]*kernel.shared_1[((threadIdx.x*24) + 193)]))
+          conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[4]*kernel.shared_1[((threadIdx.x*24) + 193)]))
+          conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[5]*kernel.shared_1[((threadIdx.x*24) + 193)]))
+          conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[6]*kernel.shared_1[((threadIdx.x*24) + 193)]))
+          conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[7]*kernel.shared_1[((threadIdx.x*24) + 193)]))
+          conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[2]*kernel.shared_1[((threadIdx.x*24) + 2)]))
+          conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[3]*kernel.shared_1[((threadIdx.x*24) + 2)]))
+          conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[4]*kernel.shared_1[((threadIdx.x*24) + 2)]))
+          conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[5]*kernel.shared_1[((threadIdx.x*24) + 2)]))
+          conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[6]*kernel.shared_1[((threadIdx.x*24) + 2)]))
+          conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[7]*kernel.shared_1[((threadIdx.x*24) + 2)]))
+          conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[8]*kernel.shared_1[((threadIdx.x*24) + 2)]))
+          conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[2]*kernel.shared_1[((threadIdx.x*24) + 194)]))
+          conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[3]*kernel.shared_1[((threadIdx.x*24) + 194)]))
+          conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[4]*kernel.shared_1[((threadIdx.x*24) + 194)]))
+          conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[5]*kernel.shared_1[((threadIdx.x*24) + 194)]))
+          conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[6]*kernel.shared_1[((threadIdx.x*24) + 194)]))
+          conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[7]*kernel.shared_1[((threadIdx.x*24) + 194)]))
+          conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[8]*kernel.shared_1[((threadIdx.x*24) + 194)]))
+          conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[9]*kernel.shared_1[((threadIdx.x*24) + 3)]))
+          conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[10]*kernel.shared_1[((threadIdx.x*24) + 3)]))
+          conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[11]*kernel.shared_1[((threadIdx.x*24) + 3)]))
+          conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[12]*kernel.shared_1[((threadIdx.x*24) + 3)]))
+          conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[13]*kernel.shared_1[((threadIdx.x*24) + 3)]))
+          conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[14]*kernel.shared_1[((threadIdx.x*24) + 3)]))
+          conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[15]*kernel.shared_1[((threadIdx.x*24) + 3)]))
+          conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[9]*kernel.shared_1[((threadIdx.x*24) + 195)]))
+          conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[10]*kernel.shared_1[((threadIdx.x*24) + 195)]))
+          conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[11]*kernel.shared_1[((threadIdx.x*24) + 195)]))
+          conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[12]*kernel.shared_1[((threadIdx.x*24) + 195)]))
+          conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[13]*kernel.shared_1[((threadIdx.x*24) + 195)]))
+          conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[14]*kernel.shared_1[((threadIdx.x*24) + 195)]))
+          conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[15]*kernel.shared_1[((threadIdx.x*24) + 195)]))
+          conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[10]*kernel.shared_1[((threadIdx.x*24) + 4)]))
+          conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[11]*kernel.shared_1[((threadIdx.x*24) + 4)]))
+          conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[12]*kernel.shared_1[((threadIdx.x*24) + 4)]))
+          conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[13]*kernel.shared_1[((threadIdx.x*24) + 4)]))
+          conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[14]*kernel.shared_1[((threadIdx.x*24) + 4)]))
+          conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[15]*kernel.shared_1[((threadIdx.x*24) + 4)]))
+          conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[16]*kernel.shared_1[((threadIdx.x*24) + 4)]))
+          conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[10]*kernel.shared_1[((threadIdx.x*24) + 196)]))
+          conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[11]*kernel.shared_1[((threadIdx.x*24) + 196)]))
+          conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[12]*kernel.shared_1[((threadIdx.x*24) + 196)]))
+          conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[13]*kernel.shared_1[((threadIdx.x*24) + 196)]))
+          conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[14]*kernel.shared_1[((threadIdx.x*24) + 196)]))
+          conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[15]*kernel.shared_1[((threadIdx.x*24) + 196)]))
+          conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[16]*kernel.shared_1[((threadIdx.x*24) + 196)]))
+          conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[11]*kernel.shared_1[((threadIdx.x*24) + 5)]))
+          conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[12]*kernel.shared_1[((threadIdx.x*24) + 5)]))
+          conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[13]*kernel.shared_1[((threadIdx.x*24) + 5)]))
+          conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[14]*kernel.shared_1[((threadIdx.x*24) + 5)]))
+          conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[15]*kernel.shared_1[((threadIdx.x*24) + 5)]))
+          conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[16]*kernel.shared_1[((threadIdx.x*24) + 5)]))
+          conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[17]*kernel.shared_1[((threadIdx.x*24) + 5)]))
+          conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[11]*kernel.shared_1[((threadIdx.x*24) + 197)]))
+          conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[12]*kernel.shared_1[((threadIdx.x*24) + 197)]))
+          conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[13]*kernel.shared_1[((threadIdx.x*24) + 197)]))
+          conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[14]*kernel.shared_1[((threadIdx.x*24) + 197)]))
+          conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[15]*kernel.shared_1[((threadIdx.x*24) + 197)]))
+          conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[16]*kernel.shared_1[((threadIdx.x*24) + 197)]))
+          conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[17]*kernel.shared_1[((threadIdx.x*24) + 197)]))
+          conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[18]*kernel.shared_1[((threadIdx.x*24) + 6)]))
+          conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[19]*kernel.shared_1[((threadIdx.x*24) + 6)]))
+          conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[20]*kernel.shared_1[((threadIdx.x*24) + 6)]))
+          conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[21]*kernel.shared_1[((threadIdx.x*24) + 6)]))
+          conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[22]*kernel.shared_1[((threadIdx.x*24) + 6)]))
+          conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[23]*kernel.shared_1[((threadIdx.x*24) + 6)]))
+          conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[24]*kernel.shared_1[((threadIdx.x*24) + 6)]))
+          conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[18]*kernel.shared_1[((threadIdx.x*24) + 198)]))
+          conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[19]*kernel.shared_1[((threadIdx.x*24) + 198)]))
+          conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[20]*kernel.shared_1[((threadIdx.x*24) + 198)]))
+          conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[21]*kernel.shared_1[((threadIdx.x*24) + 198)]))
+          conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[22]*kernel.shared_1[((threadIdx.x*24) + 198)]))
+          conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[23]*kernel.shared_1[((threadIdx.x*24) + 198)]))
+          conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[24]*kernel.shared_1[((threadIdx.x*24) + 198)]))
+          conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[19]*kernel.shared_1[((threadIdx.x*24) + 7)]))
+          conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[20]*kernel.shared_1[((threadIdx.x*24) + 7)]))
+          conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[21]*kernel.shared_1[((threadIdx.x*24) + 7)]))
+          conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[22]*kernel.shared_1[((threadIdx.x*24) + 7)]))
+          conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[23]*kernel.shared_1[((threadIdx.x*24) + 7)]))
+          conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[24]*kernel.shared_1[((threadIdx.x*24) + 7)]))
+          conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[25]*kernel.shared_1[((threadIdx.x*24) + 7)]))
+          conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[19]*kernel.shared_1[((threadIdx.x*24) + 199)]))
+          conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[20]*kernel.shared_1[((threadIdx.x*24) + 199)]))
+          conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[21]*kernel.shared_1[((threadIdx.x*24) + 199)]))
+          conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[22]*kernel.shared_1[((threadIdx.x*24) + 199)]))
+          conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[23]*kernel.shared_1[((threadIdx.x*24) + 199)]))
+          conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[24]*kernel.shared_1[((threadIdx.x*24) + 199)]))
+          conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[25]*kernel.shared_1[((threadIdx.x*24) + 199)]))
+          conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[20]*kernel.shared_1[((threadIdx.x*24) + 8)]))
+          conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[21]*kernel.shared_1[((threadIdx.x*24) + 8)]))
+          conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[22]*kernel.shared_1[((threadIdx.x*24) + 8)]))
+          conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[23]*kernel.shared_1[((threadIdx.x*24) + 8)]))
+          conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[24]*kernel.shared_1[((threadIdx.x*24) + 8)]))
+          conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[25]*kernel.shared_1[((threadIdx.x*24) + 8)]))
+          conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[26]*kernel.shared_1[((threadIdx.x*24) + 8)]))
+          conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[20]*kernel.shared_1[((threadIdx.x*24) + 200)]))
+          conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[21]*kernel.shared_1[((threadIdx.x*24) + 200)]))
+          conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[22]*kernel.shared_1[((threadIdx.x*24) + 200)]))
+          conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[23]*kernel.shared_1[((threadIdx.x*24) + 200)]))
+          conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[24]*kernel.shared_1[((threadIdx.x*24) + 200)]))
+          conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[25]*kernel.shared_1[((threadIdx.x*24) + 200)]))
+          conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[26]*kernel.shared_1[((threadIdx.x*24) + 200)]))
+          conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[27]*kernel.shared_1[((threadIdx.x*24) + 9)]))
+          conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[28]*kernel.shared_1[((threadIdx.x*24) + 9)]))
+          conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[29]*kernel.shared_1[((threadIdx.x*24) + 9)]))
+          conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[30]*kernel.shared_1[((threadIdx.x*24) + 9)]))
+          conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[31]*kernel.shared_1[((threadIdx.x*24) + 9)]))
+          conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[32]*kernel.shared_1[((threadIdx.x*24) + 9)]))
+          conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[33]*kernel.shared_1[((threadIdx.x*24) + 9)]))
+          conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[27]*kernel.shared_1[((threadIdx.x*24) + 201)]))
+          conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[28]*kernel.shared_1[((threadIdx.x*24) + 201)]))
+          conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[29]*kernel.shared_1[((threadIdx.x*24) + 201)]))
+          conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[30]*kernel.shared_1[((threadIdx.x*24) + 201)]))
+          conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[31]*kernel.shared_1[((threadIdx.x*24) + 201)]))
+          conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[32]*kernel.shared_1[((threadIdx.x*24) + 201)]))
+          conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[33]*kernel.shared_1[((threadIdx.x*24) + 201)]))
+          conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[28]*kernel.shared_1[((threadIdx.x*24) + 10)]))
+          conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[29]*kernel.shared_1[((threadIdx.x*24) + 10)]))
+          conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[30]*kernel.shared_1[((threadIdx.x*24) + 10)]))
+          conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[31]*kernel.shared_1[((threadIdx.x*24) + 10)]))
+          conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[32]*kernel.shared_1[((threadIdx.x*24) + 10)]))
+          conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[33]*kernel.shared_1[((threadIdx.x*24) + 10)]))
+          conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[34]*kernel.shared_1[((threadIdx.x*24) + 10)]))
+          conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[28]*kernel.shared_1[((threadIdx.x*24) + 202)]))
+          conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[29]*kernel.shared_1[((threadIdx.x*24) + 202)]))
+          conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[30]*kernel.shared_1[((threadIdx.x*24) + 202)]))
+          conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[31]*kernel.shared_1[((threadIdx.x*24) + 202)]))
+          conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[32]*kernel.shared_1[((threadIdx.x*24) + 202)]))
+          conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[33]*kernel.shared_1[((threadIdx.x*24) + 202)]))
+          conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[34]*kernel.shared_1[((threadIdx.x*24) + 202)]))
+          conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[29]*kernel.shared_1[((threadIdx.x*24) + 11)]))
+          conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[30]*kernel.shared_1[((threadIdx.x*24) + 11)]))
+          conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[31]*kernel.shared_1[((threadIdx.x*24) + 11)]))
+          conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[32]*kernel.shared_1[((threadIdx.x*24) + 11)]))
+          conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[33]*kernel.shared_1[((threadIdx.x*24) + 11)]))
+          conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[34]*kernel.shared_1[((threadIdx.x*24) + 11)]))
+          conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[35]*kernel.shared_1[((threadIdx.x*24) + 11)]))
+          conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[29]*kernel.shared_1[((threadIdx.x*24) + 203)]))
+          conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[30]*kernel.shared_1[((threadIdx.x*24) + 203)]))
+          conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[31]*kernel.shared_1[((threadIdx.x*24) + 203)]))
+          conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[32]*kernel.shared_1[((threadIdx.x*24) + 203)]))
+          conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[33]*kernel.shared_1[((threadIdx.x*24) + 203)]))
+          conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[34]*kernel.shared_1[((threadIdx.x*24) + 203)]))
+          conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[35]*kernel.shared_1[((threadIdx.x*24) + 203)]))
+          conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[36]*kernel.shared_1[((threadIdx.x*24) + 12)]))
+          conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[37]*kernel.shared_1[((threadIdx.x*24) + 12)]))
+          conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[38]*kernel.shared_1[((threadIdx.x*24) + 12)]))
+          conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[39]*kernel.shared_1[((threadIdx.x*24) + 12)]))
+          conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[40]*kernel.shared_1[((threadIdx.x*24) + 12)]))
+          conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[41]*kernel.shared_1[((threadIdx.x*24) + 12)]))
+          conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[42]*kernel.shared_1[((threadIdx.x*24) + 12)]))
+          conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[36]*kernel.shared_1[((threadIdx.x*24) + 204)]))
+          conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[37]*kernel.shared_1[((threadIdx.x*24) + 204)]))
+          conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[38]*kernel.shared_1[((threadIdx.x*24) + 204)]))
+          conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[39]*kernel.shared_1[((threadIdx.x*24) + 204)]))
+          conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[40]*kernel.shared_1[((threadIdx.x*24) + 204)]))
+          conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[41]*kernel.shared_1[((threadIdx.x*24) + 204)]))
+          conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[42]*kernel.shared_1[((threadIdx.x*24) + 204)]))
+          conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[37]*kernel.shared_1[((threadIdx.x*24) + 13)]))
+          conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[38]*kernel.shared_1[((threadIdx.x*24) + 13)]))
+          conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[39]*kernel.shared_1[((threadIdx.x*24) + 13)]))
+          conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[40]*kernel.shared_1[((threadIdx.x*24) + 13)]))
+          conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[41]*kernel.shared_1[((threadIdx.x*24) + 13)]))
+          conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[42]*kernel.shared_1[((threadIdx.x*24) + 13)]))
+          conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[43]*kernel.shared_1[((threadIdx.x*24) + 13)]))
+          conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[37]*kernel.shared_1[((threadIdx.x*24) + 205)]))
+          conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[38]*kernel.shared_1[((threadIdx.x*24) + 205)]))
+          conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[39]*kernel.shared_1[((threadIdx.x*24) + 205)]))
+          conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[40]*kernel.shared_1[((threadIdx.x*24) + 205)]))
+          conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[41]*kernel.shared_1[((threadIdx.x*24) + 205)]))
+          conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[42]*kernel.shared_1[((threadIdx.x*24) + 205)]))
+          conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[43]*kernel.shared_1[((threadIdx.x*24) + 205)]))
+          conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[38]*kernel.shared_1[((threadIdx.x*24) + 14)]))
+          conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[39]*kernel.shared_1[((threadIdx.x*24) + 14)]))
+          conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[40]*kernel.shared_1[((threadIdx.x*24) + 14)]))
+          conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[41]*kernel.shared_1[((threadIdx.x*24) + 14)]))
+          conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[42]*kernel.shared_1[((threadIdx.x*24) + 14)]))
+          conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[43]*kernel.shared_1[((threadIdx.x*24) + 14)]))
+          conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[44]*kernel.shared_1[((threadIdx.x*24) + 14)]))
+          conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[38]*kernel.shared_1[((threadIdx.x*24) + 206)]))
+          conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[39]*kernel.shared_1[((threadIdx.x*24) + 206)]))
+          conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[40]*kernel.shared_1[((threadIdx.x*24) + 206)]))
+          conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[41]*kernel.shared_1[((threadIdx.x*24) + 206)]))
+          conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[42]*kernel.shared_1[((threadIdx.x*24) + 206)]))
+          conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[43]*kernel.shared_1[((threadIdx.x*24) + 206)]))
+          conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[44]*kernel.shared_1[((threadIdx.x*24) + 206)]))
+          conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[45]*kernel.shared_1[((threadIdx.x*24) + 15)]))
+          conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[46]*kernel.shared_1[((threadIdx.x*24) + 15)]))
+          conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[47]*kernel.shared_1[((threadIdx.x*24) + 15)]))
+          conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[48]*kernel.shared_1[((threadIdx.x*24) + 15)]))
+          conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[49]*kernel.shared_1[((threadIdx.x*24) + 15)]))
+          conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[50]*kernel.shared_1[((threadIdx.x*24) + 15)]))
+          conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[51]*kernel.shared_1[((threadIdx.x*24) + 15)]))
+          conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[45]*kernel.shared_1[((threadIdx.x*24) + 207)]))
+          conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[46]*kernel.shared_1[((threadIdx.x*24) + 207)]))
+          conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[47]*kernel.shared_1[((threadIdx.x*24) + 207)]))
+          conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[48]*kernel.shared_1[((threadIdx.x*24) + 207)]))
+          conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[49]*kernel.shared_1[((threadIdx.x*24) + 207)]))
+          conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[50]*kernel.shared_1[((threadIdx.x*24) + 207)]))
+          conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[51]*kernel.shared_1[((threadIdx.x*24) + 207)]))
+          conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[46]*kernel.shared_1[((threadIdx.x*24) + 16)]))
+          conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[47]*kernel.shared_1[((threadIdx.x*24) + 16)]))
+          conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[48]*kernel.shared_1[((threadIdx.x*24) + 16)]))
+          conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[49]*kernel.shared_1[((threadIdx.x*24) + 16)]))
+          conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[50]*kernel.shared_1[((threadIdx.x*24) + 16)]))
+          conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[51]*kernel.shared_1[((threadIdx.x*24) + 16)]))
+          conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[52]*kernel.shared_1[((threadIdx.x*24) + 16)]))
+          conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[46]*kernel.shared_1[((threadIdx.x*24) + 208)]))
+          conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[47]*kernel.shared_1[((threadIdx.x*24) + 208)]))
+          conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[48]*kernel.shared_1[((threadIdx.x*24) + 208)]))
+          conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[49]*kernel.shared_1[((threadIdx.x*24) + 208)]))
+          conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[50]*kernel.shared_1[((threadIdx.x*24) + 208)]))
+          conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[51]*kernel.shared_1[((threadIdx.x*24) + 208)]))
+          conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[52]*kernel.shared_1[((threadIdx.x*24) + 208)]))
+          conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[47]*kernel.shared_1[((threadIdx.x*24) + 17)]))
+          conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[48]*kernel.shared_1[((threadIdx.x*24) + 17)]))
+          conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[49]*kernel.shared_1[((threadIdx.x*24) + 17)]))
+          conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[50]*kernel.shared_1[((threadIdx.x*24) + 17)]))
+          conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[51]*kernel.shared_1[((threadIdx.x*24) + 17)]))
+          conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[52]*kernel.shared_1[((threadIdx.x*24) + 17)]))
+          conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[53]*kernel.shared_1[((threadIdx.x*24) + 17)]))
+          conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[47]*kernel.shared_1[((threadIdx.x*24) + 209)]))
+          conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[48]*kernel.shared_1[((threadIdx.x*24) + 209)]))
+          conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[49]*kernel.shared_1[((threadIdx.x*24) + 209)]))
+          conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[50]*kernel.shared_1[((threadIdx.x*24) + 209)]))
+          conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[51]*kernel.shared_1[((threadIdx.x*24) + 209)]))
+          conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[52]*kernel.shared_1[((threadIdx.x*24) + 209)]))
+          conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[53]*kernel.shared_1[((threadIdx.x*24) + 209)]))
+          conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[54]*kernel.shared_1[((threadIdx.x*24) + 18)]))
+          conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[55]*kernel.shared_1[((threadIdx.x*24) + 18)]))
+          conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[56]*kernel.shared_1[((threadIdx.x*24) + 18)]))
+          conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[57]*kernel.shared_1[((threadIdx.x*24) + 18)]))
+          conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[58]*kernel.shared_1[((threadIdx.x*24) + 18)]))
+          conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[59]*kernel.shared_1[((threadIdx.x*24) + 18)]))
+          conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[60]*kernel.shared_1[((threadIdx.x*24) + 18)]))
+          conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[54]*kernel.shared_1[((threadIdx.x*24) + 210)]))
+          conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[55]*kernel.shared_1[((threadIdx.x*24) + 210)]))
+          conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[56]*kernel.shared_1[((threadIdx.x*24) + 210)]))
+          conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[57]*kernel.shared_1[((threadIdx.x*24) + 210)]))
+          conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[58]*kernel.shared_1[((threadIdx.x*24) + 210)]))
+          conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[59]*kernel.shared_1[((threadIdx.x*24) + 210)]))
+          conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[60]*kernel.shared_1[((threadIdx.x*24) + 210)]))
+          conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[55]*kernel.shared_1[((threadIdx.x*24) + 19)]))
+          conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[56]*kernel.shared_1[((threadIdx.x*24) + 19)]))
+          conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[57]*kernel.shared_1[((threadIdx.x*24) + 19)]))
+          conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[58]*kernel.shared_1[((threadIdx.x*24) + 19)]))
+          conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[59]*kernel.shared_1[((threadIdx.x*24) + 19)]))
+          conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[60]*kernel.shared_1[((threadIdx.x*24) + 19)]))
+          conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[61]*kernel.shared_1[((threadIdx.x*24) + 19)]))
+          conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[55]*kernel.shared_1[((threadIdx.x*24) + 211)]))
+          conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[56]*kernel.shared_1[((threadIdx.x*24) + 211)]))
+          conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[57]*kernel.shared_1[((threadIdx.x*24) + 211)]))
+          conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[58]*kernel.shared_1[((threadIdx.x*24) + 211)]))
+          conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[59]*kernel.shared_1[((threadIdx.x*24) + 211)]))
+          conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[60]*kernel.shared_1[((threadIdx.x*24) + 211)]))
+          conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[61]*kernel.shared_1[((threadIdx.x*24) + 211)]))
+          conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[56]*kernel.shared_1[((threadIdx.x*24) + 20)]))
+          conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[57]*kernel.shared_1[((threadIdx.x*24) + 20)]))
+          conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[58]*kernel.shared_1[((threadIdx.x*24) + 20)]))
+          conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[59]*kernel.shared_1[((threadIdx.x*24) + 20)]))
+          conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[60]*kernel.shared_1[((threadIdx.x*24) + 20)]))
+          conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[61]*kernel.shared_1[((threadIdx.x*24) + 20)]))
+          conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[62]*kernel.shared_1[((threadIdx.x*24) + 20)]))
+          conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[56]*kernel.shared_1[((threadIdx.x*24) + 212)]))
+          conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[57]*kernel.shared_1[((threadIdx.x*24) + 212)]))
+          conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[58]*kernel.shared_1[((threadIdx.x*24) + 212)]))
+          conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[59]*kernel.shared_1[((threadIdx.x*24) + 212)]))
+          conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[60]*kernel.shared_1[((threadIdx.x*24) + 212)]))
+          conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[61]*kernel.shared_1[((threadIdx.x*24) + 212)]))
+          conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[62]*kernel.shared_1[((threadIdx.x*24) + 212)]))
+          conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[63]*kernel.shared_1[((threadIdx.x*24) + 21)]))
+          conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[64]*kernel.shared_1[((threadIdx.x*24) + 21)]))
+          conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[65]*kernel.shared_1[((threadIdx.x*24) + 21)]))
+          conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[66]*kernel.shared_1[((threadIdx.x*24) + 21)]))
+          conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[67]*kernel.shared_1[((threadIdx.x*24) + 21)]))
+          conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[68]*kernel.shared_1[((threadIdx.x*24) + 21)]))
+          conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[69]*kernel.shared_1[((threadIdx.x*24) + 21)]))
+          conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[63]*kernel.shared_1[((threadIdx.x*24) + 213)]))
+          conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[64]*kernel.shared_1[((threadIdx.x*24) + 213)]))
+          conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[65]*kernel.shared_1[((threadIdx.x*24) + 213)]))
+          conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[66]*kernel.shared_1[((threadIdx.x*24) + 213)]))
+          conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[67]*kernel.shared_1[((threadIdx.x*24) + 213)]))
+          conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[68]*kernel.shared_1[((threadIdx.x*24) + 213)]))
+          conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[69]*kernel.shared_1[((threadIdx.x*24) + 213)]))
+          conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[64]*kernel.shared_1[((threadIdx.x*24) + 22)]))
+          conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[65]*kernel.shared_1[((threadIdx.x*24) + 22)]))
+          conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[66]*kernel.shared_1[((threadIdx.x*24) + 22)]))
+          conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[67]*kernel.shared_1[((threadIdx.x*24) + 22)]))
+          conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[68]*kernel.shared_1[((threadIdx.x*24) + 22)]))
+          conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[69]*kernel.shared_1[((threadIdx.x*24) + 22)]))
+          conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[70]*kernel.shared_1[((threadIdx.x*24) + 22)]))
+          conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[64]*kernel.shared_1[((threadIdx.x*24) + 214)]))
+          conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[65]*kernel.shared_1[((threadIdx.x*24) + 214)]))
+          conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[66]*kernel.shared_1[((threadIdx.x*24) + 214)]))
+          conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[67]*kernel.shared_1[((threadIdx.x*24) + 214)]))
+          conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[68]*kernel.shared_1[((threadIdx.x*24) + 214)]))
+          conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[69]*kernel.shared_1[((threadIdx.x*24) + 214)]))
+          conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[70]*kernel.shared_1[((threadIdx.x*24) + 214)]))
+          conv2d_nchw_1[0] = (conv2d_nchw_1[0] + (pad_temp.shared_1[65]*kernel.shared_1[((threadIdx.x*24) + 23)]))
+          conv2d_nchw_1[1] = (conv2d_nchw_1[1] + (pad_temp.shared_1[66]*kernel.shared_1[((threadIdx.x*24) + 23)]))
+          conv2d_nchw_1[2] = (conv2d_nchw_1[2] + (pad_temp.shared_1[67]*kernel.shared_1[((threadIdx.x*24) + 23)]))
+          conv2d_nchw_1[3] = (conv2d_nchw_1[3] + (pad_temp.shared_1[68]*kernel.shared_1[((threadIdx.x*24) + 23)]))
+          conv2d_nchw_1[4] = (conv2d_nchw_1[4] + (pad_temp.shared_1[69]*kernel.shared_1[((threadIdx.x*24) + 23)]))
+          conv2d_nchw_1[5] = (conv2d_nchw_1[5] + (pad_temp.shared_1[70]*kernel.shared_1[((threadIdx.x*24) + 23)]))
+          conv2d_nchw_1[6] = (conv2d_nchw_1[6] + (pad_temp.shared_1[71]*kernel.shared_1[((threadIdx.x*24) + 23)]))
+          conv2d_nchw_1[7] = (conv2d_nchw_1[7] + (pad_temp.shared_1[65]*kernel.shared_1[((threadIdx.x*24) + 215)]))
+          conv2d_nchw_1[8] = (conv2d_nchw_1[8] + (pad_temp.shared_1[66]*kernel.shared_1[((threadIdx.x*24) + 215)]))
+          conv2d_nchw_1[9] = (conv2d_nchw_1[9] + (pad_temp.shared_1[67]*kernel.shared_1[((threadIdx.x*24) + 215)]))
+          conv2d_nchw_1[10] = (conv2d_nchw_1[10] + (pad_temp.shared_1[68]*kernel.shared_1[((threadIdx.x*24) + 215)]))
+          conv2d_nchw_1[11] = (conv2d_nchw_1[11] + (pad_temp.shared_1[69]*kernel.shared_1[((threadIdx.x*24) + 215)]))
+          conv2d_nchw_1[12] = (conv2d_nchw_1[12] + (pad_temp.shared_1[70]*kernel.shared_1[((threadIdx.x*24) + 215)]))
+          conv2d_nchw_1[13] = (conv2d_nchw_1[13] + (pad_temp.shared_1[71]*kernel.shared_1[((threadIdx.x*24) + 215)]))
         }
       }
     }
-    for (i1.inner: int32, 0, 4) {
-      compute[((((blockIdx.x*784) + (floordiv(threadIdx.x, 7)*196)) + (i1.inner*49)) + floormod(threadIdx.x, 7))] = max((conv2d_nchw_1[i1.inner] + bias[(((blockIdx.x*16) + (floordiv(threadIdx.x, 7)*4)) + i1.inner)]), 0f32)
-      compute[(((((blockIdx.x*784) + (floordiv(threadIdx.x, 7)*196)) + (i1.inner*49)) + floormod(threadIdx.x, 7)) + 7)] = max((conv2d_nchw_1[(i1.inner + 4)] + bias[(((blockIdx.x*16) + (floordiv(threadIdx.x, 7)*4)) + i1.inner)]), 0f32)
-      compute[(((((blockIdx.x*784) + (floordiv(threadIdx.x, 7)*196)) + (i1.inner*49)) + floormod(threadIdx.x, 7)) + 14)] = max((conv2d_nchw_1[(i1.inner + 8)] + bias[(((blockIdx.x*16) + (floordiv(threadIdx.x, 7)*4)) + i1.inner)]), 0f32)
-      compute[(((((blockIdx.x*784) + (floordiv(threadIdx.x, 7)*196)) + (i1.inner*49)) + floormod(threadIdx.x, 7)) + 21)] = max((conv2d_nchw_1[(i1.inner + 12)] + bias[(((blockIdx.x*16) + (floordiv(threadIdx.x, 7)*4)) + i1.inner)]), 0f32)
-      compute[(((((blockIdx.x*784) + (floordiv(threadIdx.x, 7)*196)) + (i1.inner*49)) + floormod(threadIdx.x, 7)) + 28)] = max((conv2d_nchw_1[(i1.inner + 16)] + bias[(((blockIdx.x*16) + (floordiv(threadIdx.x, 7)*4)) + i1.inner)]), 0f32)
-      compute[(((((blockIdx.x*784) + (floordiv(threadIdx.x, 7)*196)) + (i1.inner*49)) + floormod(threadIdx.x, 7)) + 35)] = max((conv2d_nchw_1[(i1.inner + 20)] + bias[(((blockIdx.x*16) + (floordiv(threadIdx.x, 7)*4)) + i1.inner)]), 0f32)
-      compute[(((((blockIdx.x*784) + (floordiv(threadIdx.x, 7)*196)) + (i1.inner*49)) + floormod(threadIdx.x, 7)) + 42)] = max((conv2d_nchw_1[(i1.inner + 24)] + bias[(((blockIdx.x*16) + (floordiv(threadIdx.x, 7)*4)) + i1.inner)]), 0f32)
-    }
+    compute[(((floordiv(blockIdx.x, 7)*784) + (threadIdx.x*49)) + (floormod(blockIdx.x, 7)*7))] = max((conv2d_nchw_1[0] + bias[((floordiv(blockIdx.x, 7)*16) + threadIdx.x)]), 0f32)
+    compute[((((floordiv(blockIdx.x, 7)*784) + (threadIdx.x*49)) + (floormod(blockIdx.x, 7)*7)) + 1)] = max((conv2d_nchw_1[1] + bias[((floordiv(blockIdx.x, 7)*16) + threadIdx.x)]), 0f32)
+    compute[((((floordiv(blockIdx.x, 7)*784) + (threadIdx.x*49)) + (floormod(blockIdx.x, 7)*7)) + 2)] = max((conv2d_nchw_1[2] + bias[((floordiv(blockIdx.x, 7)*16) + threadIdx.x)]), 0f32)
+    compute[((((floordiv(blockIdx.x, 7)*784) + (threadIdx.x*49)) + (floormod(blockIdx.x, 7)*7)) + 3)] = max((conv2d_nchw_1[3] + bias[((floordiv(blockIdx.x, 7)*16) + threadIdx.x)]), 0f32)
+    compute[((((floordiv(blockIdx.x, 7)*784) + (threadIdx.x*49)) + (floormod(blockIdx.x, 7)*7)) + 4)] = max((conv2d_nchw_1[4] + bias[((floordiv(blockIdx.x, 7)*16) + threadIdx.x)]), 0f32)
+    compute[((((floordiv(blockIdx.x, 7)*784) + (threadIdx.x*49)) + (floormod(blockIdx.x, 7)*7)) + 5)] = max((conv2d_nchw_1[5] + bias[((floordiv(blockIdx.x, 7)*16) + threadIdx.x)]), 0f32)
+    compute[((((floordiv(blockIdx.x, 7)*784) + (threadIdx.x*49)) + (floormod(blockIdx.x, 7)*7)) + 6)] = max((conv2d_nchw_1[6] + bias[((floordiv(blockIdx.x, 7)*16) + threadIdx.x)]), 0f32)
+    compute[((((floordiv(blockIdx.x, 7)*784) + (threadIdx.x*49)) + (floormod(blockIdx.x, 7)*7)) + 392)] = max((conv2d_nchw_1[7] + bias[(((floordiv(blockIdx.x, 7)*16) + threadIdx.x) + 8)]), 0f32)
+    compute[((((floordiv(blockIdx.x, 7)*784) + (threadIdx.x*49)) + (floormod(blockIdx.x, 7)*7)) + 393)] = max((conv2d_nchw_1[8] + bias[(((floordiv(blockIdx.x, 7)*16) + threadIdx.x) + 8)]), 0f32)
+    compute[((((floordiv(blockIdx.x, 7)*784) + (threadIdx.x*49)) + (floormod(blockIdx.x, 7)*7)) + 394)] = max((conv2d_nchw_1[9] + bias[(((floordiv(blockIdx.x, 7)*16) + threadIdx.x) + 8)]), 0f32)
+    compute[((((floordiv(blockIdx.x, 7)*784) + (threadIdx.x*49)) + (floormod(blockIdx.x, 7)*7)) + 395)] = max((conv2d_nchw_1[10] + bias[(((floordiv(blockIdx.x, 7)*16) + threadIdx.x) + 8)]), 0f32)
+    compute[((((floordiv(blockIdx.x, 7)*784) + (threadIdx.x*49)) + (floormod(blockIdx.x, 7)*7)) + 396)] = max((conv2d_nchw_1[11] + bias[(((floordiv(blockIdx.x, 7)*16) + threadIdx.x) + 8)]), 0f32)
+    compute[((((floordiv(blockIdx.x, 7)*784) + (threadIdx.x*49)) + (floormod(blockIdx.x, 7)*7)) + 397)] = max((conv2d_nchw_1[12] + bias[(((floordiv(blockIdx.x, 7)*16) + threadIdx.x) + 8)]), 0f32)
+    compute[((((floordiv(blockIdx.x, 7)*784) + (threadIdx.x*49)) + (floormod(blockIdx.x, 7)*7)) + 398)] = max((conv2d_nchw_1[13] + bias[(((floordiv(blockIdx.x, 7)*16) + threadIdx.x) + 8)]), 0f32)
   }
 }
 </pre></div>
@@ -849,7 +997,7 @@ cooperative fetching, unrolling and operator fusion.</p>
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time of this operator: 0.315 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time of this operator: 0.392 ms
 </pre></div>
 </div>
 </div>
@@ -880,19 +1028,19 @@ conv2d_nchw_nn_o_o_i, conv2d_nchw_nn_o_i = s[conv2d_nchw].split(conv2d_nchw_nn_o
 conv2d_nchw_nn_o_o_o_i, conv2d_nchw_nn_o_o_i = s[conv2d_nchw].split(conv2d_nchw_nn_o_o_i, factor=1)
 conv2d_nchw_nn_o_o_o_o, conv2d_nchw_nn_o_o_o_i = s[conv2d_nchw].split(conv2d_nchw_nn_o_o_o_i, factor=1)
 conv2d_nchw_ff_o_i, conv2d_nchw_ff_i = s[conv2d_nchw].split(conv2d_nchw_ff, factor=1)
-conv2d_nchw_ff_o_o_i, conv2d_nchw_ff_o_i = s[conv2d_nchw].split(conv2d_nchw_ff_o_i, factor=4)
-conv2d_nchw_ff_o_o_o_i, conv2d_nchw_ff_o_o_i = s[conv2d_nchw].split(conv2d_nchw_ff_o_o_i, factor=4)
-conv2d_nchw_ff_o_o_o_o, conv2d_nchw_ff_o_o_o_i = s[conv2d_nchw].split(conv2d_nchw_ff_o_o_o_i, factor=1)
+conv2d_nchw_ff_o_o_i, conv2d_nchw_ff_o_i = s[conv2d_nchw].split(conv2d_nchw_ff_o_i, factor=1)
+conv2d_nchw_ff_o_o_o_i, conv2d_nchw_ff_o_o_i = s[conv2d_nchw].split(conv2d_nchw_ff_o_o_i, factor=8)
+conv2d_nchw_ff_o_o_o_o, conv2d_nchw_ff_o_o_o_i = s[conv2d_nchw].split(conv2d_nchw_ff_o_o_o_i, factor=2)
 conv2d_nchw_yy_o_i, conv2d_nchw_yy_i = s[conv2d_nchw].split(conv2d_nchw_yy, factor=1)
 conv2d_nchw_yy_o_o_i, conv2d_nchw_yy_o_i = s[conv2d_nchw].split(conv2d_nchw_yy_o_i, factor=1)
 conv2d_nchw_yy_o_o_o_i, conv2d_nchw_yy_o_o_i = s[conv2d_nchw].split(conv2d_nchw_yy_o_o_i, factor=1)
-conv2d_nchw_yy_o_o_o_o, conv2d_nchw_yy_o_o_o_i = s[conv2d_nchw].split(conv2d_nchw_yy_o_o_o_i, factor=7)
+conv2d_nchw_yy_o_o_o_o, conv2d_nchw_yy_o_o_o_i = s[conv2d_nchw].split(conv2d_nchw_yy_o_o_o_i, factor=1)
 conv2d_nchw_xx_o_i, conv2d_nchw_xx_i = s[conv2d_nchw].split(conv2d_nchw_xx, factor=1)
 conv2d_nchw_xx_o_o_i, conv2d_nchw_xx_o_i = s[conv2d_nchw].split(conv2d_nchw_xx_o_i, factor=1)
-conv2d_nchw_xx_o_o_o_i, conv2d_nchw_xx_o_o_i = s[conv2d_nchw].split(conv2d_nchw_xx_o_o_i, factor=7)
-conv2d_nchw_xx_o_o_o_o, conv2d_nchw_xx_o_o_o_i = s[conv2d_nchw].split(conv2d_nchw_xx_o_o_o_i, factor=1)
-conv2d_nchw_rc_o_i, conv2d_nchw_rc_i = s[conv2d_nchw].split(conv2d_nchw_rc, factor=8)
-conv2d_nchw_rc_o_o, conv2d_nchw_rc_o_i = s[conv2d_nchw].split(conv2d_nchw_rc_o_i, factor=1)
+conv2d_nchw_xx_o_o_o_i, conv2d_nchw_xx_o_o_i = s[conv2d_nchw].split(conv2d_nchw_xx_o_o_i, factor=1)
+conv2d_nchw_xx_o_o_o_o, conv2d_nchw_xx_o_o_o_i = s[conv2d_nchw].split(conv2d_nchw_xx_o_o_o_i, factor=7)
+conv2d_nchw_rc_o_i, conv2d_nchw_rc_i = s[conv2d_nchw].split(conv2d_nchw_rc, factor=1)
+conv2d_nchw_rc_o_o, conv2d_nchw_rc_o_i = s[conv2d_nchw].split(conv2d_nchw_rc_o_i, factor=8)
 conv2d_nchw_ry_o_i, conv2d_nchw_ry_i = s[conv2d_nchw].split(conv2d_nchw_ry, factor=1)
 conv2d_nchw_ry_o_o, conv2d_nchw_ry_o_i = s[conv2d_nchw].split(conv2d_nchw_ry_o_i, factor=1)
 conv2d_nchw_rx_o_i, conv2d_nchw_rx_i = s[conv2d_nchw].split(conv2d_nchw_rx, factor=1)
@@ -901,15 +1049,15 @@ s[conv2d_nchw].reorder(conv2d_nchw_nn_o_o_o_o, conv2d_nchw_ff_o_o_o_o, conv2d_nc
 compute_i0_o_i, compute_i0_i = s[compute].split(compute_i0, factor=1)
 compute_i0_o_o_i, compute_i0_o_i = s[compute].split(compute_i0_o_i, factor=1)
 compute_i0_o_o_o, compute_i0_o_o_i = s[compute].split(compute_i0_o_o_i, factor=1)
-compute_i1_o_i, compute_i1_i = s[compute].split(compute_i1, factor=4)
-compute_i1_o_o_i, compute_i1_o_i = s[compute].split(compute_i1_o_i, factor=4)
-compute_i1_o_o_o, compute_i1_o_o_i = s[compute].split(compute_i1_o_o_i, factor=1)
+compute_i1_o_i, compute_i1_i = s[compute].split(compute_i1, factor=1)
+compute_i1_o_o_i, compute_i1_o_i = s[compute].split(compute_i1_o_i, factor=8)
+compute_i1_o_o_o, compute_i1_o_o_i = s[compute].split(compute_i1_o_o_i, factor=2)
 compute_i2_o_i, compute_i2_i = s[compute].split(compute_i2, factor=1)
 compute_i2_o_o_i, compute_i2_o_i = s[compute].split(compute_i2_o_i, factor=1)
-compute_i2_o_o_o, compute_i2_o_o_i = s[compute].split(compute_i2_o_o_i, factor=7)
+compute_i2_o_o_o, compute_i2_o_o_i = s[compute].split(compute_i2_o_o_i, factor=1)
 compute_i3_o_i, compute_i3_i = s[compute].split(compute_i3, factor=1)
-compute_i3_o_o_i, compute_i3_o_i = s[compute].split(compute_i3_o_i, factor=7)
-compute_i3_o_o_o, compute_i3_o_o_i = s[compute].split(compute_i3_o_o_i, factor=1)
+compute_i3_o_o_i, compute_i3_o_i = s[compute].split(compute_i3_o_i, factor=1)
+compute_i3_o_o_o, compute_i3_o_o_i = s[compute].split(compute_i3_o_o_i, factor=7)
 s[compute].reorder(compute_i0_o_o_o, compute_i1_o_o_o, compute_i2_o_o_o, compute_i3_o_o_o, compute_i0_o_o_i, compute_i1_o_o_i, compute_i2_o_o_i, compute_i3_o_o_i, compute_i0_o_i, compute_i1_o_i, compute_i2_o_i, compute_i3_o_i, compute_i0_i, compute_i1_i, compute_i2_i, compute_i3_i)
 s[conv2d_nchw].compute_at(s[compute], compute_i3_o_i)
 kernel_shared = s.cache_read(kernel, &quot;shared&quot;, [conv2d_nchw])
@@ -928,12 +1076,12 @@ s[compute].bind(compute_i0_o_i_i1_o_i_fused_i2_o_i_fused_i3_o_i_fused, te.thread
 kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused = s[kernel_shared].fuse(kernel_shared_ax0, kernel_shared_ax1, kernel_shared_ax2, kernel_shared_ax3)
 kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o, kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_i = s[kernel_shared].split(kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused, factor=1)
 s[kernel_shared].vectorize(kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_i)
-kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_o, kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_i = s[kernel_shared].split(kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o, factor=28)
+kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_o, kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_i = s[kernel_shared].split(kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o, factor=8)
 s[kernel_shared].bind(kernel_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_i, te.thread_axis(&quot;threadIdx.x&quot;))
 pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused = s[pad_temp_shared].fuse(pad_temp_shared_ax0, pad_temp_shared_ax1, pad_temp_shared_ax2, pad_temp_shared_ax3)
 pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o, pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_i = s[pad_temp_shared].split(pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused, factor=1)
 s[pad_temp_shared].vectorize(pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_i)
-pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_o, pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_i = s[pad_temp_shared].split(pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o, factor=28)
+pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_o, pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_i = s[pad_temp_shared].split(pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o, factor=8)
 s[pad_temp_shared].bind(pad_temp_shared_ax0_ax1_fused_ax2_fused_ax3_fused_o_i, te.thread_axis(&quot;threadIdx.x&quot;))
 s[conv2d_nchw].pragma(conv2d_nchw_nn_o_o_o_o, &quot;auto_unroll_max_step&quot;, 512)
 s[conv2d_nchw].pragma(conv2d_nchw_nn_o_o_o_o, &quot;unroll_explicit&quot;, True)
@@ -953,313 +1101,437 @@ CUDA source code:
   #define int64_t long long
   #define uint64_t unsigned long long
 #endif
-extern &quot;C&quot; __global__ void __launch_bounds__(28) default_function_kernel0(float* __restrict__ data, float* __restrict__ kernel, float* __restrict__ compute, float* __restrict__ bias) {
-  float conv2d_nchw[28];
-  __shared__ float pad_temp_shared[504];
+extern &quot;C&quot; __global__ void __launch_bounds__(8) default_function_kernel0(float* __restrict__ data, float* __restrict__ kernel, float* __restrict__ compute, float* __restrict__ bias) {
+  float conv2d_nchw[14];
+  __shared__ float pad_temp_shared[72];
   __shared__ float kernel_shared[384];
   conv2d_nchw[0] = 0.000000e+00f;
-  conv2d_nchw[4] = 0.000000e+00f;
-  conv2d_nchw[8] = 0.000000e+00f;
-  conv2d_nchw[12] = 0.000000e+00f;
-  conv2d_nchw[16] = 0.000000e+00f;
-  conv2d_nchw[20] = 0.000000e+00f;
-  conv2d_nchw[24] = 0.000000e+00f;
   conv2d_nchw[1] = 0.000000e+00f;
-  conv2d_nchw[5] = 0.000000e+00f;
-  conv2d_nchw[9] = 0.000000e+00f;
-  conv2d_nchw[13] = 0.000000e+00f;
-  conv2d_nchw[17] = 0.000000e+00f;
-  conv2d_nchw[21] = 0.000000e+00f;
-  conv2d_nchw[25] = 0.000000e+00f;
   conv2d_nchw[2] = 0.000000e+00f;
-  conv2d_nchw[6] = 0.000000e+00f;
-  conv2d_nchw[10] = 0.000000e+00f;
-  conv2d_nchw[14] = 0.000000e+00f;
-  conv2d_nchw[18] = 0.000000e+00f;
-  conv2d_nchw[22] = 0.000000e+00f;
-  conv2d_nchw[26] = 0.000000e+00f;
   conv2d_nchw[3] = 0.000000e+00f;
+  conv2d_nchw[4] = 0.000000e+00f;
+  conv2d_nchw[5] = 0.000000e+00f;
+  conv2d_nchw[6] = 0.000000e+00f;
   conv2d_nchw[7] = 0.000000e+00f;
+  conv2d_nchw[8] = 0.000000e+00f;
+  conv2d_nchw[9] = 0.000000e+00f;
+  conv2d_nchw[10] = 0.000000e+00f;
   conv2d_nchw[11] = 0.000000e+00f;
-  conv2d_nchw[15] = 0.000000e+00f;
-  conv2d_nchw[19] = 0.000000e+00f;
-  conv2d_nchw[23] = 0.000000e+00f;
-  conv2d_nchw[27] = 0.000000e+00f;
+  conv2d_nchw[12] = 0.000000e+00f;
+  conv2d_nchw[13] = 0.000000e+00f;
   for (int rc_outer_outer = 0; rc_outer_outer &lt; 64; ++rc_outer_outer) {
     for (int ry_outer_outer = 0; ry_outer_outer &lt; 3; ++ry_outer_outer) {
       __syncthreads();
-      pad_temp_shared[((int)threadIdx.x)] = ((((1 &lt;= ((((int)threadIdx.x) / 9) + ry_outer_outer)) &amp;&amp; (1 &lt;= (((int)threadIdx.x) % 9))) &amp;&amp; ((((int)threadIdx.x) % 9) &lt; 8)) ? data[(((((rc_outer_outer * 392) + ((((int)threadIdx.x) / 9) * 7)) + (ry_outer_outer * 7)) + (((int)threadIdx.x) % 9)) - 8)] : 0.000000e+00f);
-      pad_temp_shared[(((int)threadIdx.x) + 28)] = (((((((((int)threadIdx.x) + 28) / 9) + ry_outer_outer) &lt; 8) &amp;&amp; (1 &lt;= ((((int)threadIdx.x) + 1) % 9))) &amp;&amp; (((((int)threadIdx.x) + 1) % 9) &lt; 8)) ? data[(((((rc_outer_outer * 392) + (((((int)threadIdx.x) + 28) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 1) % 9)) - 8)] : 0.000000e+00f);
-      pad_temp_shared[(((int)threadIdx.x) + 56)] = (((((1 &lt;= ((((((int)threadIdx.x) + 56) % 63) / 9) + ry_outer_outer)) &amp;&amp; (((((((int)threadIdx.x) + 56) % 63) / 9) + ry_outer_outer) &lt; 8)) &amp;&amp; (1 &lt;= ((((int)threadIdx.x) + 2) % 9))) &amp;&amp; (((((int)threadIdx.x) + 2) % 9) &lt; 8)) ? data[(((((rc_outer_outer * 392) + (((((int)threadIdx.x) + 56) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 2) % 9)) - 8)] : 0.000000e+00f);
-      pad_temp_shared[(((int)threadIdx.x) + 84)] = (((1 &lt;= ((((int)threadIdx.x) + 3) % 9)) &amp;&amp; (((((int)threadIdx.x) + 3) % 9) &lt; 8)) ? data[(((((rc_outer_outer * 392) + (((((int)threadIdx.x) + 84) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 3) % 9)) - 8)] : 0.000000e+00f);
-      pad_temp_shared[(((int)threadIdx.x) + 112)] = (((((1 &lt;= ((((((int)threadIdx.x) + 49) % 63) / 9) + ry_outer_outer)) &amp;&amp; (((((((int)threadIdx.x) + 49) % 63) / 9) + ry_outer_outer) &lt; 8)) &amp;&amp; (1 &lt;= ((((int)threadIdx.x) + 4) % 9))) &amp;&amp; (((((int)threadIdx.x) + 4) % 9) &lt; 8)) ? data[(((((rc_outer_outer * 392) + (((((int)threadIdx.x) + 112) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 4) % 9)) - 8)] : 0.000000e+00f);
-      pad_temp_shared[(((int)threadIdx.x) + 140)] = (((1 &lt;= ((((int)threadIdx.x) + 5) % 9)) &amp;&amp; (((((int)threadIdx.x) + 5) % 9) &lt; 8)) ? data[(((((rc_outer_outer * 392) + (((((int)threadIdx.x) + 140) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 5) % 9)) - 8)] : 0.000000e+00f);
-      pad_temp_shared[(((int)threadIdx.x) + 168)] = (((((1 &lt;= ((((((int)threadIdx.x) + 42) % 63) / 9) + ry_outer_outer)) &amp;&amp; (((((((int)threadIdx.x) + 42) % 63) / 9) + ry_outer_outer) &lt; 8)) &amp;&amp; (1 &lt;= ((((int)threadIdx.x) + 6) % 9))) &amp;&amp; (((((int)threadIdx.x) + 6) % 9) &lt; 8)) ? data[(((((rc_outer_outer * 392) + (((((int)threadIdx.x) + 168) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 6) % 9)) - 8)] : 0.000000e+00f);
-      pad_temp_shared[(((int)threadIdx.x) + 196)] = ((((1 &lt;= ((((((int)threadIdx.x) + 7) % 63) / 9) + ry_outer_outer)) &amp;&amp; (1 &lt;= ((((int)threadIdx.x) + 7) % 9))) &amp;&amp; (((((int)threadIdx.x) + 7) % 9) &lt; 8)) ? data[(((((rc_outer_outer * 392) + (((((int)threadIdx.x) + 196) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 7) % 9)) - 8)] : 0.000000e+00f);
-      pad_temp_shared[(((int)threadIdx.x) + 224)] = ((((((((((int)threadIdx.x) + 35) % 63) / 9) + ry_outer_outer) &lt; 8) &amp;&amp; (1 &lt;= ((((int)threadIdx.x) + 8) % 9))) &amp;&amp; (((((int)threadIdx.x) + 8) % 9) &lt; 8)) ? data[(((((rc_outer_outer * 392) + (((((int)threadIdx.x) + 224) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 8) % 9)) - 8)] : 0.000000e+00f);
-      pad_temp_shared[(((int)threadIdx.x) + 252)] = ((((1 &lt;= ((((int)threadIdx.x) / 9) + ry_outer_outer)) &amp;&amp; (1 &lt;= (((int)threadIdx.x) % 9))) &amp;&amp; ((((int)threadIdx.x) % 9) &lt; 8)) ? data[(((((rc_outer_outer * 392) + ((((int)threadIdx.x) / 9) * 7)) + (ry_outer_outer * 7)) + (((int)threadIdx.x) % 9)) + 188)] : 0.000000e+00f);
-      pad_temp_shared[(((int)threadIdx.x) + 280)] = ((((((((((int)threadIdx.x) + 28) % 63) / 9) + ry_outer_outer) &lt; 8) &amp;&amp; (1 &lt;= ((((int)threadIdx.x) + 1) % 9))) &amp;&amp; (((((int)threadIdx.x) + 1) % 9) &lt; 8)) ? data[(((((rc_outer_outer * 392) + (((((int)threadIdx.x) + 280) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 1) % 9)) - 8)] : 0.000000e+00f);
-      pad_temp_shared[(((int)threadIdx.x) + 308)] = (((((1 &lt;= ((((((int)threadIdx.x) + 56) % 63) / 9) + ry_outer_outer)) &amp;&amp; (((((((int)threadIdx.x) + 56) % 63) / 9) + ry_outer_outer) &lt; 8)) &amp;&amp; (1 &lt;= ((((int)threadIdx.x) + 2) % 9))) &amp;&amp; (((((int)threadIdx.x) + 2) % 9) &lt; 8)) ? data[(((((rc_outer_outer * 392) + (((((int)threadIdx.x) + 308) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 2) % 9)) - 8)] : 0.000000e+00f);
-      pad_temp_shared[(((int)threadIdx.x) + 336)] = (((1 &lt;= ((((int)threadIdx.x) + 3) % 9)) &amp;&amp; (((((int)threadIdx.x) + 3) % 9) &lt; 8)) ? data[(((((rc_outer_outer * 392) + (((((int)threadIdx.x) + 336) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 3) % 9)) - 8)] : 0.000000e+00f);
-      pad_temp_shared[(((int)threadIdx.x) + 364)] = (((((1 &lt;= ((((((int)threadIdx.x) + 49) % 63) / 9) + ry_outer_outer)) &amp;&amp; (((((((int)threadIdx.x) + 49) % 63) / 9) + ry_outer_outer) &lt; 8)) &amp;&amp; (1 &lt;= ((((int)threadIdx.x) + 4) % 9))) &amp;&amp; (((((int)threadIdx.x) + 4) % 9) &lt; 8)) ? data[(((((rc_outer_outer * 392) + (((((int)threadIdx.x) + 364) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 4) % 9)) - 8)] : 0.000000e+00f);
-      pad_temp_shared[(((int)threadIdx.x) + 392)] = (((1 &lt;= ((((int)threadIdx.x) + 5) % 9)) &amp;&amp; (((((int)threadIdx.x) + 5) % 9) &lt; 8)) ? data[(((((rc_outer_outer * 392) + (((((int)threadIdx.x) + 392) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 5) % 9)) - 8)] : 0.000000e+00f);
-      pad_temp_shared[(((int)threadIdx.x) + 420)] = (((((1 &lt;= ((((((int)threadIdx.x) + 42) % 63) / 9) + ry_outer_outer)) &amp;&amp; (((((((int)threadIdx.x) + 42) % 63) / 9) + ry_outer_outer) &lt; 8)) &amp;&amp; (1 &lt;= ((((int)threadIdx.x) + 6) % 9))) &amp;&amp; (((((int)threadIdx.x) + 6) % 9) &lt; 8)) ? data[(((((rc_outer_outer * 392) + (((((int)threadIdx.x) + 420) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 6) % 9)) - 8)] : 0.000000e+00f);
-      pad_temp_shared[(((int)threadIdx.x) + 448)] = ((((1 &lt;= ((((((int)threadIdx.x) + 7) % 63) / 9) + ry_outer_outer)) &amp;&amp; (1 &lt;= ((((int)threadIdx.x) + 7) % 9))) &amp;&amp; (((((int)threadIdx.x) + 7) % 9) &lt; 8)) ? data[(((((rc_outer_outer * 392) + (((((int)threadIdx.x) + 448) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 7) % 9)) - 8)] : 0.000000e+00f);
-      pad_temp_shared[(((int)threadIdx.x) + 476)] = ((((((((((int)threadIdx.x) + 35) % 63) / 9) + ry_outer_outer) &lt; 8) &amp;&amp; (1 &lt;= ((((int)threadIdx.x) + 8) % 9))) &amp;&amp; (((((int)threadIdx.x) + 8) % 9) &lt; 8)) ? data[(((((rc_outer_outer * 392) + (((((int)threadIdx.x) + 476) / 9) * 7)) + (ry_outer_outer * 7)) + ((((int)threadIdx.x) + 8) % 9)) - 8)] : 0.000000e+00f);
-      kernel_shared[((int)threadIdx.x)] = kernel[((((((((int)blockIdx.x) * 73728) + ((((int)threadIdx.x) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3))];
-      kernel_shared[(((int)threadIdx.x) + 28)] = kernel[((((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 28) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 4) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
-      kernel_shared[(((int)threadIdx.x) + 56)] = kernel[((((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 56) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 8) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
-      kernel_shared[(((int)threadIdx.x) + 84)] = kernel[((((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 84) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) / 3) + 4) &amp; 7) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3))];
-      kernel_shared[(((int)threadIdx.x) + 112)] = kernel[((((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 112) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 16) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
-      kernel_shared[(((int)threadIdx.x) + 140)] = kernel[((((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 140) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 20) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
-      kernel_shared[(((int)threadIdx.x) + 168)] = kernel[(((((((((int)blockIdx.x) * 73728) + ((((int)threadIdx.x) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 32256)];
-      kernel_shared[(((int)threadIdx.x) + 196)] = kernel[((((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 196) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 4) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
-      kernel_shared[(((int)threadIdx.x) + 224)] = kernel[((((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 224) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 8) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
-      kernel_shared[(((int)threadIdx.x) + 252)] = kernel[((((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 252) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) / 3) + 4) &amp; 7) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3))];
-      kernel_shared[(((int)threadIdx.x) + 280)] = kernel[((((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 280) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 16) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
-      kernel_shared[(((int)threadIdx.x) + 308)] = kernel[((((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 308) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 20) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
-      kernel_shared[(((int)threadIdx.x) + 336)] = kernel[(((((((((int)blockIdx.x) * 73728) + ((((int)threadIdx.x) / 24) * 4608)) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 64512)];
-      if (((int)threadIdx.x) &lt; 20) {
-        kernel_shared[(((int)threadIdx.x) + 364)] = kernel[((((((((int)blockIdx.x) * 73728) + (((((int)threadIdx.x) + 364) / 24) * 4608)) + (rc_outer_outer * 72)) + ((((((int)threadIdx.x) + 4) % 24) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
-      }
+      pad_temp_shared[((int)threadIdx.x)] = ((((1 &lt;= (ry_outer_outer + (((int)blockIdx.x) % 7))) &amp;&amp; ((ry_outer_outer + (((int)blockIdx.x) % 7)) &lt; 8)) &amp;&amp; (1 &lt;= ((int)threadIdx.x))) ? data[(((((rc_outer_outer * 392) + (ry_outer_outer * 7)) + ((((int)blockIdx.x) % 7) * 7)) + ((int)threadIdx.x)) - 8)] : 0.000000e+00f);
+      pad_temp_shared[(((int)threadIdx.x) + 8)] = (((((1 &lt;= (ry_outer_outer + (((int)blockIdx.x) % 7))) &amp;&amp; ((ry_outer_outer + (((int)blockIdx.x) % 7)) &lt; 8)) &amp;&amp; (1 &lt;= ((((int)threadIdx.x) + 8) % 9))) &amp;&amp; (((((int)threadIdx.x) + 8) % 9) &lt; 8)) ? data[((((((rc_outer_outer * 392) + (((((int)threadIdx.x) + 8) / 9) * 49)) + (ry_outer_outer * 7)) + ((((int)blockIdx.x) % 7) * 7)) + ((((int)threadIdx.x) + 8) % 9)) - 8)] : 0.000000e+00f);
+      pad_temp_shared[(((int)threadIdx.x) + 16)] = (((((1 &lt;= (ry_outer_outer + (((int)blockIdx.x) % 7))) &amp;&amp; ((ry_outer_outer + (((int)blockIdx.x) % 7)) &lt; 8)) &amp;&amp; (1 &lt;= ((((int)threadIdx.x) + 7) % 9))) &amp;&amp; (((((int)threadIdx.x) + 7) % 9) &lt; 8)) ? data[((((((rc_outer_outer * 392) + (((((int)threadIdx.x) + 16) / 9) * 49)) + (ry_outer_outer * 7)) + ((((int)blockIdx.x) % 7) * 7)) + ((((int)threadIdx.x) + 7) % 9)) - 8)] : 0.000000e+00f);
+      pad_temp_shared[(((int)threadIdx.x) + 24)] = (((((1 &lt;= (ry_outer_outer + (((int)blockIdx.x) % 7))) &amp;&amp; ((ry_outer_outer + (((int)blockIdx.x) % 7)) &lt; 8)) &amp;&amp; (1 &lt;= ((((int)threadIdx.x) + 6) % 9))) &amp;&amp; (((((int)threadIdx.x) + 6) % 9) &lt; 8)) ? data[((((((rc_outer_outer * 392) + (((((int)threadIdx.x) + 24) / 9) * 49)) + (ry_outer_outer * 7)) + ((((int)blockIdx.x) % 7) * 7)) + ((((int)threadIdx.x) + 6) % 9)) - 8)] : 0.000000e+00f);
+      pad_temp_shared[(((int)threadIdx.x) + 32)] = (((((1 &lt;= (ry_outer_outer + (((int)blockIdx.x) % 7))) &amp;&amp; ((ry_outer_outer + (((int)blockIdx.x) % 7)) &lt; 8)) &amp;&amp; (1 &lt;= ((((int)threadIdx.x) + 5) % 9))) &amp;&amp; (((((int)threadIdx.x) + 5) % 9) &lt; 8)) ? data[((((((rc_outer_outer * 392) + (((((int)threadIdx.x) + 32) / 9) * 49)) + (ry_outer_outer * 7)) + ((((int)blockIdx.x) % 7) * 7)) + ((((int)threadIdx.x) + 5) % 9)) - 8)] : 0.000000e+00f);
+      pad_temp_shared[(((int)threadIdx.x) + 40)] = (((((1 &lt;= (ry_outer_outer + (((int)blockIdx.x) % 7))) &amp;&amp; ((ry_outer_outer + (((int)blockIdx.x) % 7)) &lt; 8)) &amp;&amp; (1 &lt;= ((((int)threadIdx.x) + 4) % 9))) &amp;&amp; (((((int)threadIdx.x) + 4) % 9) &lt; 8)) ? data[((((((rc_outer_outer * 392) + (((((int)threadIdx.x) + 40) / 9) * 49)) + (ry_outer_outer * 7)) + ((((int)blockIdx.x) % 7) * 7)) + ((((int)threadIdx.x) + 4) % 9)) - 8)] : 0.000000e+00f);
+      pad_temp_shared[(((int)threadIdx.x) + 48)] = (((((1 &lt;= (ry_outer_outer + (((int)blockIdx.x) % 7))) &amp;&amp; ((ry_outer_outer + (((int)blockIdx.x) % 7)) &lt; 8)) &amp;&amp; (1 &lt;= ((((int)threadIdx.x) + 3) % 9))) &amp;&amp; (((((int)threadIdx.x) + 3) % 9) &lt; 8)) ? data[((((((rc_outer_outer * 392) + (((((int)threadIdx.x) + 48) / 9) * 49)) + (ry_outer_outer * 7)) + ((((int)blockIdx.x) % 7) * 7)) + ((((int)threadIdx.x) + 3) % 9)) - 8)] : 0.000000e+00f);
+      pad_temp_shared[(((int)threadIdx.x) + 56)] = (((((1 &lt;= (ry_outer_outer + (((int)blockIdx.x) % 7))) &amp;&amp; ((ry_outer_outer + (((int)blockIdx.x) % 7)) &lt; 8)) &amp;&amp; (1 &lt;= ((((int)threadIdx.x) + 2) % 9))) &amp;&amp; (((((int)threadIdx.x) + 2) % 9) &lt; 8)) ? data[((((((rc_outer_outer * 392) + (((((int)threadIdx.x) + 56) / 9) * 49)) + (ry_outer_outer * 7)) + ((((int)blockIdx.x) % 7) * 7)) + ((((int)threadIdx.x) + 2) % 9)) - 8)] : 0.000000e+00f);
+      pad_temp_shared[(((int)threadIdx.x) + 64)] = ((((1 &lt;= (ry_outer_outer + (((int)blockIdx.x) % 7))) &amp;&amp; ((ry_outer_outer + (((int)blockIdx.x) % 7)) &lt; 8)) &amp;&amp; (((int)threadIdx.x) &lt; 7)) ? data[((((((rc_outer_outer * 392) + (((((int)threadIdx.x) + 64) / 9) * 49)) + (ry_outer_outer * 7)) + ((((int)blockIdx.x) % 7) * 7)) + (((int)threadIdx.x) + 1)) - 8)] : 0.000000e+00f);
+      kernel_shared[((int)threadIdx.x)] = kernel[((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + ((((int)threadIdx.x) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3))];
+      kernel_shared[(((int)threadIdx.x) + 8)] = kernel[((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 8) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3))];
+      kernel_shared[(((int)threadIdx.x) + 16)] = kernel[((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 16) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3))];
+      kernel_shared[(((int)threadIdx.x) + 24)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + ((((int)threadIdx.x) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 4608)];
+      kernel_shared[(((int)threadIdx.x) + 32)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 8) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3)) + 4608)];
+      kernel_shared[(((int)threadIdx.x) + 40)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 16) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3)) + 4608)];
+      kernel_shared[(((int)threadIdx.x) + 48)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + ((((int)threadIdx.x) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 9216)];
+      kernel_shared[(((int)threadIdx.x) + 56)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 8) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3)) + 9216)];
+      kernel_shared[(((int)threadIdx.x) + 64)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 16) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3)) + 9216)];
+      kernel_shared[(((int)threadIdx.x) + 72)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + ((((int)threadIdx.x) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 13824)];
+      kernel_shared[(((int)threadIdx.x) + 80)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 8) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3)) + 13824)];
+      kernel_shared[(((int)threadIdx.x) + 88)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 16) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3)) + 13824)];
+      kernel_shared[(((int)threadIdx.x) + 96)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + ((((int)threadIdx.x) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 18432)];
+      kernel_shared[(((int)threadIdx.x) + 104)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 8) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3)) + 18432)];
+      kernel_shared[(((int)threadIdx.x) + 112)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 16) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3)) + 18432)];
+      kernel_shared[(((int)threadIdx.x) + 120)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + ((((int)threadIdx.x) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 23040)];
+      kernel_shared[(((int)threadIdx.x) + 128)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 8) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3)) + 23040)];
+      kernel_shared[(((int)threadIdx.x) + 136)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 16) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3)) + 23040)];
+      kernel_shared[(((int)threadIdx.x) + 144)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + ((((int)threadIdx.x) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 27648)];
+      kernel_shared[(((int)threadIdx.x) + 152)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 8) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3)) + 27648)];
+      kernel_shared[(((int)threadIdx.x) + 160)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 16) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3)) + 27648)];
+      kernel_shared[(((int)threadIdx.x) + 168)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + ((((int)threadIdx.x) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 32256)];
+      kernel_shared[(((int)threadIdx.x) + 176)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 8) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3)) + 32256)];
+      kernel_shared[(((int)threadIdx.x) + 184)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 16) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3)) + 32256)];
+      kernel_shared[(((int)threadIdx.x) + 192)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + ((((int)threadIdx.x) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 36864)];
+      kernel_shared[(((int)threadIdx.x) + 200)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 8) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3)) + 36864)];
+      kernel_shared[(((int)threadIdx.x) + 208)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 16) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3)) + 36864)];
+      kernel_shared[(((int)threadIdx.x) + 216)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + ((((int)threadIdx.x) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 41472)];
+      kernel_shared[(((int)threadIdx.x) + 224)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 8) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3)) + 41472)];
+      kernel_shared[(((int)threadIdx.x) + 232)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 16) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3)) + 41472)];
+      kernel_shared[(((int)threadIdx.x) + 240)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + ((((int)threadIdx.x) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 46080)];
+      kernel_shared[(((int)threadIdx.x) + 248)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 8) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3)) + 46080)];
+      kernel_shared[(((int)threadIdx.x) + 256)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 16) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3)) + 46080)];
+      kernel_shared[(((int)threadIdx.x) + 264)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + ((((int)threadIdx.x) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 50688)];
+      kernel_shared[(((int)threadIdx.x) + 272)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 8) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3)) + 50688)];
+      kernel_shared[(((int)threadIdx.x) + 280)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 16) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3)) + 50688)];
+      kernel_shared[(((int)threadIdx.x) + 288)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + ((((int)threadIdx.x) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 55296)];
+      kernel_shared[(((int)threadIdx.x) + 296)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 8) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3)) + 55296)];
+      kernel_shared[(((int)threadIdx.x) + 304)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 16) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3)) + 55296)];
+      kernel_shared[(((int)threadIdx.x) + 312)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + ((((int)threadIdx.x) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 59904)];
+      kernel_shared[(((int)threadIdx.x) + 320)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 8) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3)) + 59904)];
+      kernel_shared[(((int)threadIdx.x) + 328)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 16) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3)) + 59904)];
+      kernel_shared[(((int)threadIdx.x) + 336)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + ((((int)threadIdx.x) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 64512)];
+      kernel_shared[(((int)threadIdx.x) + 344)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 8) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3)) + 64512)];
+      kernel_shared[(((int)threadIdx.x) + 352)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 16) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3)) + 64512)];
+      kernel_shared[(((int)threadIdx.x) + 360)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + ((((int)threadIdx.x) / 3) * 9)) + (ry_outer_outer * 3)) + (((int)threadIdx.x) % 3)) + 69120)];
+      kernel_shared[(((int)threadIdx.x) + 368)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 8) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 2) % 3)) + 69120)];
+      kernel_shared[(((int)threadIdx.x) + 376)] = kernel[(((((((((int)blockIdx.x) / 7) * 73728) + (rc_outer_outer * 72)) + (((((int)threadIdx.x) + 16) / 3) * 9)) + (ry_outer_outer * 3)) + ((((int)threadIdx.x) + 1) % 3)) + 69120)];
       __syncthreads();
-      for (int rx_outer_inner = 0; rx_outer_inner &lt; 3; ++rx_outer_inner) {
-        conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[(rx_outer_inner + (((int)threadIdx.x) % 7))] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + rx_outer_inner)]));
-        conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 9)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + rx_outer_inner)]));
-        conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 18)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + rx_outer_inner)]));
-        conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 27)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + rx_outer_inner)]));
-        conv2d_nchw[16] = (conv2d_nchw[16] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 36)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + rx_outer_inner)]));
-        conv2d_nchw[20] = (conv2d_nchw[20] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 45)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + rx_outer_inner)]));
-        conv2d_nchw[24] = (conv2d_nchw[24] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 54)] * kernel_shared[(((((int)threadIdx.x) / 7) * 96) + rx_outer_inner)]));
-        conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 63)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 3)]));
-        conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 72)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 3)]));
-        conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 81)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 3)]));
-        conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 90)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 3)]));
-        conv2d_nchw[16] = (conv2d_nchw[16] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 99)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 3)]));
-        conv2d_nchw[20] = (conv2d_nchw[20] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 108)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 3)]));
-        conv2d_nchw[24] = (conv2d_nchw[24] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 117)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 3)]));
-        conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 126)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 6)]));
-        conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 135)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 6)]));
-        conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 144)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 6)]));
-        conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 153)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 6)]));
-        conv2d_nchw[16] = (conv2d_nchw[16] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 162)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 6)]));
-        conv2d_nchw[20] = (conv2d_nchw[20] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 171)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 6)]));
-        conv2d_nchw[24] = (conv2d_nchw[24] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 180)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 6)]));
-        conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 189)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 9)]));
-        conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 198)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 9)]));
-        conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 207)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 9)]));
-        conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 216)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 9)]));
-        conv2d_nchw[16] = (conv2d_nchw[16] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 225)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 9)]));
-        conv2d_nchw[20] = (conv2d_nchw[20] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 234)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 9)]));
-        conv2d_nchw[24] = (conv2d_nchw[24] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 243)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 9)]));
-        conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 252)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 12)]));
-        conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 261)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 12)]));
-        conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 270)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 12)]));
-        conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 279)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 12)]));
-        conv2d_nchw[16] = (conv2d_nchw[16] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 288)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 12)]));
-        conv2d_nchw[20] = (conv2d_nchw[20] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 297)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 12)]));
-        conv2d_nchw[24] = (conv2d_nchw[24] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 306)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 12)]));
-        conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 315)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 15)]));
-        conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 324)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 15)]));
-        conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 333)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 15)]));
-        conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 342)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 15)]));
-        conv2d_nchw[16] = (conv2d_nchw[16] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 351)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 15)]));
-        conv2d_nchw[20] = (conv2d_nchw[20] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 360)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 15)]));
-        conv2d_nchw[24] = (conv2d_nchw[24] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 369)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 15)]));
-        conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 378)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 18)]));
-        conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 387)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 18)]));
-        conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 396)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 18)]));
-        conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 405)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 18)]));
-        conv2d_nchw[16] = (conv2d_nchw[16] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 414)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 18)]));
-        conv2d_nchw[20] = (conv2d_nchw[20] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 423)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 18)]));
-        conv2d_nchw[24] = (conv2d_nchw[24] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 432)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 18)]));
-        conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 441)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 21)]));
-        conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 450)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 21)]));
-        conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 459)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 21)]));
-        conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 468)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 21)]));
-        conv2d_nchw[16] = (conv2d_nchw[16] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 477)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 21)]));
-        conv2d_nchw[20] = (conv2d_nchw[20] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 486)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 21)]));
-        conv2d_nchw[24] = (conv2d_nchw[24] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 495)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 21)]));
-        conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[(rx_outer_inner + (((int)threadIdx.x) % 7))] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 24)]));
-        conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 9)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 24)]));
-        conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 18)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 24)]));
-        conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 27)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 24)]));
-        conv2d_nchw[17] = (conv2d_nchw[17] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 36)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 24)]));
-        conv2d_nchw[21] = (conv2d_nchw[21] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 45)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 24)]));
-        conv2d_nchw[25] = (conv2d_nchw[25] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 54)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 24)]));
-        conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 63)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 27)]));
-        conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 72)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 27)]));
-        conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 81)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 27)]));
-        conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 90)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 27)]));
-        conv2d_nchw[17] = (conv2d_nchw[17] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 99)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 27)]));
-        conv2d_nchw[21] = (conv2d_nchw[21] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 108)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 27)]));
-        conv2d_nchw[25] = (conv2d_nchw[25] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 117)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 27)]));
-        conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 126)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 30)]));
-        conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 135)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 30)]));
-        conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 144)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 30)]));
-        conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 153)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 30)]));
-        conv2d_nchw[17] = (conv2d_nchw[17] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 162)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 30)]));
-        conv2d_nchw[21] = (conv2d_nchw[21] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 171)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 30)]));
-        conv2d_nchw[25] = (conv2d_nchw[25] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 180)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 30)]));
-        conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 189)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 33)]));
-        conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 198)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 33)]));
-        conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 207)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 33)]));
-        conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 216)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 33)]));
-        conv2d_nchw[17] = (conv2d_nchw[17] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 225)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 33)]));
-        conv2d_nchw[21] = (conv2d_nchw[21] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 234)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 33)]));
-        conv2d_nchw[25] = (conv2d_nchw[25] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 243)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 33)]));
-        conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 252)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 36)]));
-        conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 261)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 36)]));
-        conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 270)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 36)]));
-        conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 279)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 36)]));
-        conv2d_nchw[17] = (conv2d_nchw[17] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 288)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 36)]));
-        conv2d_nchw[21] = (conv2d_nchw[21] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 297)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 36)]));
-        conv2d_nchw[25] = (conv2d_nchw[25] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 306)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 36)]));
-        conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 315)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 39)]));
-        conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 324)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 39)]));
-        conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 333)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 39)]));
-        conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 342)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 39)]));
-        conv2d_nchw[17] = (conv2d_nchw[17] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 351)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 39)]));
-        conv2d_nchw[21] = (conv2d_nchw[21] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 360)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 39)]));
-        conv2d_nchw[25] = (conv2d_nchw[25] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 369)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 39)]));
-        conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 378)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 42)]));
-        conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 387)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 42)]));
-        conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 396)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 42)]));
-        conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 405)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 42)]));
-        conv2d_nchw[17] = (conv2d_nchw[17] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 414)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 42)]));
-        conv2d_nchw[21] = (conv2d_nchw[21] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 423)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 42)]));
-        conv2d_nchw[25] = (conv2d_nchw[25] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 432)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 42)]));
-        conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 441)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 45)]));
-        conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 450)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 45)]));
-        conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 459)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 45)]));
-        conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 468)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 45)]));
-        conv2d_nchw[17] = (conv2d_nchw[17] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 477)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 45)]));
-        conv2d_nchw[21] = (conv2d_nchw[21] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 486)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 45)]));
-        conv2d_nchw[25] = (conv2d_nchw[25] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 495)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 45)]));
-        conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[(rx_outer_inner + (((int)threadIdx.x) % 7))] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 48)]));
-        conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 9)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 48)]));
-        conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 18)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 48)]));
-        conv2d_nchw[14] = (conv2d_nchw[14] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 27)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 48)]));
-        conv2d_nchw[18] = (conv2d_nchw[18] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 36)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 48)]));
-        conv2d_nchw[22] = (conv2d_nchw[22] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 45)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 48)]));
-        conv2d_nchw[26] = (conv2d_nchw[26] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 54)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 48)]));
-        conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 63)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 51)]));
-        conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 72)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 51)]));
-        conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 81)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 51)]));
-        conv2d_nchw[14] = (conv2d_nchw[14] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 90)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 51)]));
-        conv2d_nchw[18] = (conv2d_nchw[18] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 99)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 51)]));
-        conv2d_nchw[22] = (conv2d_nchw[22] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 108)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 51)]));
-        conv2d_nchw[26] = (conv2d_nchw[26] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 117)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 51)]));
-        conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 126)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 54)]));
-        conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 135)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 54)]));
-        conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 144)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 54)]));
-        conv2d_nchw[14] = (conv2d_nchw[14] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 153)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 54)]));
-        conv2d_nchw[18] = (conv2d_nchw[18] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 162)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 54)]));
-        conv2d_nchw[22] = (conv2d_nchw[22] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 171)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 54)]));
-        conv2d_nchw[26] = (conv2d_nchw[26] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 180)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 54)]));
-        conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 189)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 57)]));
-        conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 198)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 57)]));
-        conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 207)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 57)]));
-        conv2d_nchw[14] = (conv2d_nchw[14] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 216)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 57)]));
-        conv2d_nchw[18] = (conv2d_nchw[18] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 225)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 57)]));
-        conv2d_nchw[22] = (conv2d_nchw[22] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 234)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 57)]));
-        conv2d_nchw[26] = (conv2d_nchw[26] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 243)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 57)]));
-        conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 252)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 60)]));
-        conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 261)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 60)]));
-        conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 270)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 60)]));
-        conv2d_nchw[14] = (conv2d_nchw[14] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 279)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 60)]));
-        conv2d_nchw[18] = (conv2d_nchw[18] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 288)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 60)]));
-        conv2d_nchw[22] = (conv2d_nchw[22] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 297)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 60)]));
-        conv2d_nchw[26] = (conv2d_nchw[26] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 306)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 60)]));
-        conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 315)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 63)]));
-        conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 324)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 63)]));
-        conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 333)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 63)]));
-        conv2d_nchw[14] = (conv2d_nchw[14] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 342)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 63)]));
-        conv2d_nchw[18] = (conv2d_nchw[18] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 351)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 63)]));
-        conv2d_nchw[22] = (conv2d_nchw[22] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 360)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 63)]));
-        conv2d_nchw[26] = (conv2d_nchw[26] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 369)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 63)]));
-        conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 378)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 66)]));
-        conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 387)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 66)]));
-        conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 396)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 66)]));
-        conv2d_nchw[14] = (conv2d_nchw[14] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 405)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 66)]));
-        conv2d_nchw[18] = (conv2d_nchw[18] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 414)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 66)]));
-        conv2d_nchw[22] = (conv2d_nchw[22] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 423)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 66)]));
-        conv2d_nchw[26] = (conv2d_nchw[26] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 432)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 66)]));
-        conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 441)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 69)]));
-        conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 450)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 69)]));
-        conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 459)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 69)]));
-        conv2d_nchw[14] = (conv2d_nchw[14] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 468)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 69)]));
-        conv2d_nchw[18] = (conv2d_nchw[18] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 477)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 69)]));
-        conv2d_nchw[22] = (conv2d_nchw[22] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 486)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 69)]));
-        conv2d_nchw[26] = (conv2d_nchw[26] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 495)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 69)]));
-        conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[(rx_outer_inner + (((int)threadIdx.x) % 7))] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 72)]));
-        conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 9)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 72)]));
-        conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 18)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 72)]));
-        conv2d_nchw[15] = (conv2d_nchw[15] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 27)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 72)]));
-        conv2d_nchw[19] = (conv2d_nchw[19] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 36)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 72)]));
-        conv2d_nchw[23] = (conv2d_nchw[23] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 45)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 72)]));
-        conv2d_nchw[27] = (conv2d_nchw[27] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 54)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 72)]));
-        conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 63)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 75)]));
-        conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 72)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 75)]));
-        conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 81)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 75)]));
-        conv2d_nchw[15] = (conv2d_nchw[15] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 90)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 75)]));
-        conv2d_nchw[19] = (conv2d_nchw[19] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 99)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 75)]));
-        conv2d_nchw[23] = (conv2d_nchw[23] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 108)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 75)]));
-        conv2d_nchw[27] = (conv2d_nchw[27] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 117)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 75)]));
-        conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 126)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 78)]));
-        conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 135)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 78)]));
-        conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 144)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 78)]));
-        conv2d_nchw[15] = (conv2d_nchw[15] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 153)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 78)]));
-        conv2d_nchw[19] = (conv2d_nchw[19] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 162)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 78)]));
-        conv2d_nchw[23] = (conv2d_nchw[23] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 171)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 78)]));
-        conv2d_nchw[27] = (conv2d_nchw[27] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 180)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 78)]));
-        conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 189)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 81)]));
-        conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 198)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 81)]));
-        conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 207)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 81)]));
-        conv2d_nchw[15] = (conv2d_nchw[15] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 216)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 81)]));
-        conv2d_nchw[19] = (conv2d_nchw[19] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 225)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 81)]));
-        conv2d_nchw[23] = (conv2d_nchw[23] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 234)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 81)]));
-        conv2d_nchw[27] = (conv2d_nchw[27] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 243)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 81)]));
-        conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 252)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 84)]));
-        conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 261)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 84)]));
-        conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 270)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 84)]));
-        conv2d_nchw[15] = (conv2d_nchw[15] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 279)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 84)]));
-        conv2d_nchw[19] = (conv2d_nchw[19] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 288)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 84)]));
-        conv2d_nchw[23] = (conv2d_nchw[23] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 297)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 84)]));
-        conv2d_nchw[27] = (conv2d_nchw[27] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 306)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 84)]));
-        conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 315)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 87)]));
-        conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 324)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 87)]));
-        conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 333)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 87)]));
-        conv2d_nchw[15] = (conv2d_nchw[15] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 342)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 87)]));
-        conv2d_nchw[19] = (conv2d_nchw[19] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 351)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 87)]));
-        conv2d_nchw[23] = (conv2d_nchw[23] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 360)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 87)]));
-        conv2d_nchw[27] = (conv2d_nchw[27] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 369)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 87)]));
-        conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 378)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 90)]));
-        conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 387)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 90)]));
-        conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 396)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 90)]));
-        conv2d_nchw[15] = (conv2d_nchw[15] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 405)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 90)]));
-        conv2d_nchw[19] = (conv2d_nchw[19] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 414)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 90)]));
-        conv2d_nchw[23] = (conv2d_nchw[23] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 423)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 90)]));
-        conv2d_nchw[27] = (conv2d_nchw[27] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 432)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 90)]));
-        conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 441)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 93)]));
-        conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 450)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 93)]));
-        conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 459)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 93)]));
-        conv2d_nchw[15] = (conv2d_nchw[15] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 468)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 93)]));
-        conv2d_nchw[19] = (conv2d_nchw[19] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 477)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 93)]));
-        conv2d_nchw[23] = (conv2d_nchw[23] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 486)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 93)]));
-        conv2d_nchw[27] = (conv2d_nchw[27] + (pad_temp_shared[((rx_outer_inner + (((int)threadIdx.x) % 7)) + 495)] * kernel_shared[((((((int)threadIdx.x) / 7) * 96) + rx_outer_inner) + 93)]));
-      }
+      conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[0] * kernel_shared[(((int)threadIdx.x) * 24)]));
+      conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[1] * kernel_shared[(((int)threadIdx.x) * 24)]));
+      conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[2] * kernel_shared[(((int)threadIdx.x) * 24)]));
+      conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[3] * kernel_shared[(((int)threadIdx.x) * 24)]));
+      conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[4] * kernel_shared[(((int)threadIdx.x) * 24)]));
+      conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[5] * kernel_shared[(((int)threadIdx.x) * 24)]));
+      conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[6] * kernel_shared[(((int)threadIdx.x) * 24)]));
+      conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[0] * kernel_shared[((((int)threadIdx.x) * 24) + 192)]));
+      conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[1] * kernel_shared[((((int)threadIdx.x) * 24) + 192)]));
+      conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[2] * kernel_shared[((((int)threadIdx.x) * 24) + 192)]));
+      conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[3] * kernel_shared[((((int)threadIdx.x) * 24) + 192)]));
+      conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[4] * kernel_shared[((((int)threadIdx.x) * 24) + 192)]));
+      conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[5] * kernel_shared[((((int)threadIdx.x) * 24) + 192)]));
+      conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[6] * kernel_shared[((((int)threadIdx.x) * 24) + 192)]));
+      conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[1] * kernel_shared[((((int)threadIdx.x) * 24) + 1)]));
+      conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[2] * kernel_shared[((((int)threadIdx.x) * 24) + 1)]));
+      conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[3] * kernel_shared[((((int)threadIdx.x) * 24) + 1)]));
+      conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[4] * kernel_shared[((((int)threadIdx.x) * 24) + 1)]));
+      conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[5] * kernel_shared[((((int)threadIdx.x) * 24) + 1)]));
+      conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[6] * kernel_shared[((((int)threadIdx.x) * 24) + 1)]));
+      conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[7] * kernel_shared[((((int)threadIdx.x) * 24) + 1)]));
+      conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[1] * kernel_shared[((((int)threadIdx.x) * 24) + 193)]));
+      conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[2] * kernel_shared[((((int)threadIdx.x) * 24) + 193)]));
+      conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[3] * kernel_shared[((((int)threadIdx.x) * 24) + 193)]));
+      conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[4] * kernel_shared[((((int)threadIdx.x) * 24) + 193)]));
+      conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[5] * kernel_shared[((((int)threadIdx.x) * 24) + 193)]));
+      conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[6] * kernel_shared[((((int)threadIdx.x) * 24) + 193)]));
+      conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[7] * kernel_shared[((((int)threadIdx.x) * 24) + 193)]));
+      conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[2] * kernel_shared[((((int)threadIdx.x) * 24) + 2)]));
+      conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[3] * kernel_shared[((((int)threadIdx.x) * 24) + 2)]));
+      conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[4] * kernel_shared[((((int)threadIdx.x) * 24) + 2)]));
+      conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[5] * kernel_shared[((((int)threadIdx.x) * 24) + 2)]));
+      conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[6] * kernel_shared[((((int)threadIdx.x) * 24) + 2)]));
+      conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[7] * kernel_shared[((((int)threadIdx.x) * 24) + 2)]));
+      conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[8] * kernel_shared[((((int)threadIdx.x) * 24) + 2)]));
+      conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[2] * kernel_shared[((((int)threadIdx.x) * 24) + 194)]));
+      conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[3] * kernel_shared[((((int)threadIdx.x) * 24) + 194)]));
+      conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[4] * kernel_shared[((((int)threadIdx.x) * 24) + 194)]));
+      conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[5] * kernel_shared[((((int)threadIdx.x) * 24) + 194)]));
+      conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[6] * kernel_shared[((((int)threadIdx.x) * 24) + 194)]));
+      conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[7] * kernel_shared[((((int)threadIdx.x) * 24) + 194)]));
+      conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[8] * kernel_shared[((((int)threadIdx.x) * 24) + 194)]));
+      conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[9] * kernel_shared[((((int)threadIdx.x) * 24) + 3)]));
+      conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[10] * kernel_shared[((((int)threadIdx.x) * 24) + 3)]));
+      conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[11] * kernel_shared[((((int)threadIdx.x) * 24) + 3)]));
+      conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[12] * kernel_shared[((((int)threadIdx.x) * 24) + 3)]));
+      conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[13] * kernel_shared[((((int)threadIdx.x) * 24) + 3)]));
+      conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[14] * kernel_shared[((((int)threadIdx.x) * 24) + 3)]));
+      conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[15] * kernel_shared[((((int)threadIdx.x) * 24) + 3)]));
+      conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[9] * kernel_shared[((((int)threadIdx.x) * 24) + 195)]));
+      conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[10] * kernel_shared[((((int)threadIdx.x) * 24) + 195)]));
+      conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[11] * kernel_shared[((((int)threadIdx.x) * 24) + 195)]));
+      conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[12] * kernel_shared[((((int)threadIdx.x) * 24) + 195)]));
+      conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[13] * kernel_shared[((((int)threadIdx.x) * 24) + 195)]));
+      conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[14] * kernel_shared[((((int)threadIdx.x) * 24) + 195)]));
+      conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[15] * kernel_shared[((((int)threadIdx.x) * 24) + 195)]));
+      conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[10] * kernel_shared[((((int)threadIdx.x) * 24) + 4)]));
+      conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[11] * kernel_shared[((((int)threadIdx.x) * 24) + 4)]));
+      conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[12] * kernel_shared[((((int)threadIdx.x) * 24) + 4)]));
+      conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[13] * kernel_shared[((((int)threadIdx.x) * 24) + 4)]));
+      conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[14] * kernel_shared[((((int)threadIdx.x) * 24) + 4)]));
+      conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[15] * kernel_shared[((((int)threadIdx.x) * 24) + 4)]));
+      conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[16] * kernel_shared[((((int)threadIdx.x) * 24) + 4)]));
+      conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[10] * kernel_shared[((((int)threadIdx.x) * 24) + 196)]));
+      conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[11] * kernel_shared[((((int)threadIdx.x) * 24) + 196)]));
+      conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[12] * kernel_shared[((((int)threadIdx.x) * 24) + 196)]));
+      conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[13] * kernel_shared[((((int)threadIdx.x) * 24) + 196)]));
+      conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[14] * kernel_shared[((((int)threadIdx.x) * 24) + 196)]));
+      conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[15] * kernel_shared[((((int)threadIdx.x) * 24) + 196)]));
+      conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[16] * kernel_shared[((((int)threadIdx.x) * 24) + 196)]));
+      conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[11] * kernel_shared[((((int)threadIdx.x) * 24) + 5)]));
+      conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[12] * kernel_shared[((((int)threadIdx.x) * 24) + 5)]));
+      conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[13] * kernel_shared[((((int)threadIdx.x) * 24) + 5)]));
+      conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[14] * kernel_shared[((((int)threadIdx.x) * 24) + 5)]));
+      conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[15] * kernel_shared[((((int)threadIdx.x) * 24) + 5)]));
+      conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[16] * kernel_shared[((((int)threadIdx.x) * 24) + 5)]));
+      conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[17] * kernel_shared[((((int)threadIdx.x) * 24) + 5)]));
+      conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[11] * kernel_shared[((((int)threadIdx.x) * 24) + 197)]));
+      conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[12] * kernel_shared[((((int)threadIdx.x) * 24) + 197)]));
+      conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[13] * kernel_shared[((((int)threadIdx.x) * 24) + 197)]));
+      conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[14] * kernel_shared[((((int)threadIdx.x) * 24) + 197)]));
+      conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[15] * kernel_shared[((((int)threadIdx.x) * 24) + 197)]));
+      conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[16] * kernel_shared[((((int)threadIdx.x) * 24) + 197)]));
+      conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[17] * kernel_shared[((((int)threadIdx.x) * 24) + 197)]));
+      conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[18] * kernel_shared[((((int)threadIdx.x) * 24) + 6)]));
+      conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[19] * kernel_shared[((((int)threadIdx.x) * 24) + 6)]));
+      conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[20] * kernel_shared[((((int)threadIdx.x) * 24) + 6)]));
+      conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[21] * kernel_shared[((((int)threadIdx.x) * 24) + 6)]));
+      conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[22] * kernel_shared[((((int)threadIdx.x) * 24) + 6)]));
+      conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[23] * kernel_shared[((((int)threadIdx.x) * 24) + 6)]));
+      conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[24] * kernel_shared[((((int)threadIdx.x) * 24) + 6)]));
+      conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[18] * kernel_shared[((((int)threadIdx.x) * 24) + 198)]));
+      conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[19] * kernel_shared[((((int)threadIdx.x) * 24) + 198)]));
+      conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[20] * kernel_shared[((((int)threadIdx.x) * 24) + 198)]));
+      conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[21] * kernel_shared[((((int)threadIdx.x) * 24) + 198)]));
+      conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[22] * kernel_shared[((((int)threadIdx.x) * 24) + 198)]));
+      conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[23] * kernel_shared[((((int)threadIdx.x) * 24) + 198)]));
+      conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[24] * kernel_shared[((((int)threadIdx.x) * 24) + 198)]));
+      conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[19] * kernel_shared[((((int)threadIdx.x) * 24) + 7)]));
+      conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[20] * kernel_shared[((((int)threadIdx.x) * 24) + 7)]));
+      conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[21] * kernel_shared[((((int)threadIdx.x) * 24) + 7)]));
+      conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[22] * kernel_shared[((((int)threadIdx.x) * 24) + 7)]));
+      conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[23] * kernel_shared[((((int)threadIdx.x) * 24) + 7)]));
+      conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[24] * kernel_shared[((((int)threadIdx.x) * 24) + 7)]));
+      conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[25] * kernel_shared[((((int)threadIdx.x) * 24) + 7)]));
+      conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[19] * kernel_shared[((((int)threadIdx.x) * 24) + 199)]));
+      conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[20] * kernel_shared[((((int)threadIdx.x) * 24) + 199)]));
+      conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[21] * kernel_shared[((((int)threadIdx.x) * 24) + 199)]));
+      conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[22] * kernel_shared[((((int)threadIdx.x) * 24) + 199)]));
+      conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[23] * kernel_shared[((((int)threadIdx.x) * 24) + 199)]));
+      conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[24] * kernel_shared[((((int)threadIdx.x) * 24) + 199)]));
+      conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[25] * kernel_shared[((((int)threadIdx.x) * 24) + 199)]));
+      conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[20] * kernel_shared[((((int)threadIdx.x) * 24) + 8)]));
+      conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[21] * kernel_shared[((((int)threadIdx.x) * 24) + 8)]));
+      conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[22] * kernel_shared[((((int)threadIdx.x) * 24) + 8)]));
+      conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[23] * kernel_shared[((((int)threadIdx.x) * 24) + 8)]));
+      conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[24] * kernel_shared[((((int)threadIdx.x) * 24) + 8)]));
+      conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[25] * kernel_shared[((((int)threadIdx.x) * 24) + 8)]));
+      conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[26] * kernel_shared[((((int)threadIdx.x) * 24) + 8)]));
+      conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[20] * kernel_shared[((((int)threadIdx.x) * 24) + 200)]));
+      conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[21] * kernel_shared[((((int)threadIdx.x) * 24) + 200)]));
+      conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[22] * kernel_shared[((((int)threadIdx.x) * 24) + 200)]));
+      conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[23] * kernel_shared[((((int)threadIdx.x) * 24) + 200)]));
+      conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[24] * kernel_shared[((((int)threadIdx.x) * 24) + 200)]));
+      conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[25] * kernel_shared[((((int)threadIdx.x) * 24) + 200)]));
+      conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[26] * kernel_shared[((((int)threadIdx.x) * 24) + 200)]));
+      conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[27] * kernel_shared[((((int)threadIdx.x) * 24) + 9)]));
+      conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[28] * kernel_shared[((((int)threadIdx.x) * 24) + 9)]));
+      conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[29] * kernel_shared[((((int)threadIdx.x) * 24) + 9)]));
+      conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[30] * kernel_shared[((((int)threadIdx.x) * 24) + 9)]));
+      conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[31] * kernel_shared[((((int)threadIdx.x) * 24) + 9)]));
+      conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[32] * kernel_shared[((((int)threadIdx.x) * 24) + 9)]));
+      conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[33] * kernel_shared[((((int)threadIdx.x) * 24) + 9)]));
+      conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[27] * kernel_shared[((((int)threadIdx.x) * 24) + 201)]));
+      conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[28] * kernel_shared[((((int)threadIdx.x) * 24) + 201)]));
+      conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[29] * kernel_shared[((((int)threadIdx.x) * 24) + 201)]));
+      conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[30] * kernel_shared[((((int)threadIdx.x) * 24) + 201)]));
+      conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[31] * kernel_shared[((((int)threadIdx.x) * 24) + 201)]));
+      conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[32] * kernel_shared[((((int)threadIdx.x) * 24) + 201)]));
+      conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[33] * kernel_shared[((((int)threadIdx.x) * 24) + 201)]));
+      conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[28] * kernel_shared[((((int)threadIdx.x) * 24) + 10)]));
+      conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[29] * kernel_shared[((((int)threadIdx.x) * 24) + 10)]));
+      conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[30] * kernel_shared[((((int)threadIdx.x) * 24) + 10)]));
+      conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[31] * kernel_shared[((((int)threadIdx.x) * 24) + 10)]));
+      conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[32] * kernel_shared[((((int)threadIdx.x) * 24) + 10)]));
+      conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[33] * kernel_shared[((((int)threadIdx.x) * 24) + 10)]));
+      conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[34] * kernel_shared[((((int)threadIdx.x) * 24) + 10)]));
+      conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[28] * kernel_shared[((((int)threadIdx.x) * 24) + 202)]));
+      conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[29] * kernel_shared[((((int)threadIdx.x) * 24) + 202)]));
+      conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[30] * kernel_shared[((((int)threadIdx.x) * 24) + 202)]));
+      conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[31] * kernel_shared[((((int)threadIdx.x) * 24) + 202)]));
+      conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[32] * kernel_shared[((((int)threadIdx.x) * 24) + 202)]));
+      conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[33] * kernel_shared[((((int)threadIdx.x) * 24) + 202)]));
+      conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[34] * kernel_shared[((((int)threadIdx.x) * 24) + 202)]));
+      conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[29] * kernel_shared[((((int)threadIdx.x) * 24) + 11)]));
+      conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[30] * kernel_shared[((((int)threadIdx.x) * 24) + 11)]));
+      conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[31] * kernel_shared[((((int)threadIdx.x) * 24) + 11)]));
+      conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[32] * kernel_shared[((((int)threadIdx.x) * 24) + 11)]));
+      conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[33] * kernel_shared[((((int)threadIdx.x) * 24) + 11)]));
+      conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[34] * kernel_shared[((((int)threadIdx.x) * 24) + 11)]));
+      conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[35] * kernel_shared[((((int)threadIdx.x) * 24) + 11)]));
+      conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[29] * kernel_shared[((((int)threadIdx.x) * 24) + 203)]));
+      conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[30] * kernel_shared[((((int)threadIdx.x) * 24) + 203)]));
+      conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[31] * kernel_shared[((((int)threadIdx.x) * 24) + 203)]));
+      conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[32] * kernel_shared[((((int)threadIdx.x) * 24) + 203)]));
+      conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[33] * kernel_shared[((((int)threadIdx.x) * 24) + 203)]));
+      conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[34] * kernel_shared[((((int)threadIdx.x) * 24) + 203)]));
+      conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[35] * kernel_shared[((((int)threadIdx.x) * 24) + 203)]));
+      conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[36] * kernel_shared[((((int)threadIdx.x) * 24) + 12)]));
+      conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[37] * kernel_shared[((((int)threadIdx.x) * 24) + 12)]));
+      conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[38] * kernel_shared[((((int)threadIdx.x) * 24) + 12)]));
+      conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[39] * kernel_shared[((((int)threadIdx.x) * 24) + 12)]));
+      conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[40] * kernel_shared[((((int)threadIdx.x) * 24) + 12)]));
+      conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[41] * kernel_shared[((((int)threadIdx.x) * 24) + 12)]));
+      conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[42] * kernel_shared[((((int)threadIdx.x) * 24) + 12)]));
+      conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[36] * kernel_shared[((((int)threadIdx.x) * 24) + 204)]));
+      conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[37] * kernel_shared[((((int)threadIdx.x) * 24) + 204)]));
+      conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[38] * kernel_shared[((((int)threadIdx.x) * 24) + 204)]));
+      conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[39] * kernel_shared[((((int)threadIdx.x) * 24) + 204)]));
+      conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[40] * kernel_shared[((((int)threadIdx.x) * 24) + 204)]));
+      conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[41] * kernel_shared[((((int)threadIdx.x) * 24) + 204)]));
+      conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[42] * kernel_shared[((((int)threadIdx.x) * 24) + 204)]));
+      conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[37] * kernel_shared[((((int)threadIdx.x) * 24) + 13)]));
+      conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[38] * kernel_shared[((((int)threadIdx.x) * 24) + 13)]));
+      conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[39] * kernel_shared[((((int)threadIdx.x) * 24) + 13)]));
+      conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[40] * kernel_shared[((((int)threadIdx.x) * 24) + 13)]));
+      conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[41] * kernel_shared[((((int)threadIdx.x) * 24) + 13)]));
+      conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[42] * kernel_shared[((((int)threadIdx.x) * 24) + 13)]));
+      conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[43] * kernel_shared[((((int)threadIdx.x) * 24) + 13)]));
+      conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[37] * kernel_shared[((((int)threadIdx.x) * 24) + 205)]));
+      conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[38] * kernel_shared[((((int)threadIdx.x) * 24) + 205)]));
+      conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[39] * kernel_shared[((((int)threadIdx.x) * 24) + 205)]));
+      conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[40] * kernel_shared[((((int)threadIdx.x) * 24) + 205)]));
+      conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[41] * kernel_shared[((((int)threadIdx.x) * 24) + 205)]));
+      conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[42] * kernel_shared[((((int)threadIdx.x) * 24) + 205)]));
+      conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[43] * kernel_shared[((((int)threadIdx.x) * 24) + 205)]));
+      conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[38] * kernel_shared[((((int)threadIdx.x) * 24) + 14)]));
+      conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[39] * kernel_shared[((((int)threadIdx.x) * 24) + 14)]));
+      conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[40] * kernel_shared[((((int)threadIdx.x) * 24) + 14)]));
+      conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[41] * kernel_shared[((((int)threadIdx.x) * 24) + 14)]));
+      conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[42] * kernel_shared[((((int)threadIdx.x) * 24) + 14)]));
+      conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[43] * kernel_shared[((((int)threadIdx.x) * 24) + 14)]));
+      conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[44] * kernel_shared[((((int)threadIdx.x) * 24) + 14)]));
+      conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[38] * kernel_shared[((((int)threadIdx.x) * 24) + 206)]));
+      conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[39] * kernel_shared[((((int)threadIdx.x) * 24) + 206)]));
+      conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[40] * kernel_shared[((((int)threadIdx.x) * 24) + 206)]));
+      conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[41] * kernel_shared[((((int)threadIdx.x) * 24) + 206)]));
+      conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[42] * kernel_shared[((((int)threadIdx.x) * 24) + 206)]));
+      conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[43] * kernel_shared[((((int)threadIdx.x) * 24) + 206)]));
+      conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[44] * kernel_shared[((((int)threadIdx.x) * 24) + 206)]));
+      conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[45] * kernel_shared[((((int)threadIdx.x) * 24) + 15)]));
+      conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[46] * kernel_shared[((((int)threadIdx.x) * 24) + 15)]));
+      conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[47] * kernel_shared[((((int)threadIdx.x) * 24) + 15)]));
+      conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[48] * kernel_shared[((((int)threadIdx.x) * 24) + 15)]));
+      conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[49] * kernel_shared[((((int)threadIdx.x) * 24) + 15)]));
+      conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[50] * kernel_shared[((((int)threadIdx.x) * 24) + 15)]));
+      conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[51] * kernel_shared[((((int)threadIdx.x) * 24) + 15)]));
+      conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[45] * kernel_shared[((((int)threadIdx.x) * 24) + 207)]));
+      conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[46] * kernel_shared[((((int)threadIdx.x) * 24) + 207)]));
+      conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[47] * kernel_shared[((((int)threadIdx.x) * 24) + 207)]));
+      conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[48] * kernel_shared[((((int)threadIdx.x) * 24) + 207)]));
+      conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[49] * kernel_shared[((((int)threadIdx.x) * 24) + 207)]));
+      conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[50] * kernel_shared[((((int)threadIdx.x) * 24) + 207)]));
+      conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[51] * kernel_shared[((((int)threadIdx.x) * 24) + 207)]));
+      conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[46] * kernel_shared[((((int)threadIdx.x) * 24) + 16)]));
+      conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[47] * kernel_shared[((((int)threadIdx.x) * 24) + 16)]));
+      conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[48] * kernel_shared[((((int)threadIdx.x) * 24) + 16)]));
+      conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[49] * kernel_shared[((((int)threadIdx.x) * 24) + 16)]));
+      conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[50] * kernel_shared[((((int)threadIdx.x) * 24) + 16)]));
+      conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[51] * kernel_shared[((((int)threadIdx.x) * 24) + 16)]));
+      conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[52] * kernel_shared[((((int)threadIdx.x) * 24) + 16)]));
+      conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[46] * kernel_shared[((((int)threadIdx.x) * 24) + 208)]));
+      conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[47] * kernel_shared[((((int)threadIdx.x) * 24) + 208)]));
+      conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[48] * kernel_shared[((((int)threadIdx.x) * 24) + 208)]));
+      conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[49] * kernel_shared[((((int)threadIdx.x) * 24) + 208)]));
+      conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[50] * kernel_shared[((((int)threadIdx.x) * 24) + 208)]));
+      conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[51] * kernel_shared[((((int)threadIdx.x) * 24) + 208)]));
+      conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[52] * kernel_shared[((((int)threadIdx.x) * 24) + 208)]));
+      conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[47] * kernel_shared[((((int)threadIdx.x) * 24) + 17)]));
+      conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[48] * kernel_shared[((((int)threadIdx.x) * 24) + 17)]));
+      conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[49] * kernel_shared[((((int)threadIdx.x) * 24) + 17)]));
+      conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[50] * kernel_shared[((((int)threadIdx.x) * 24) + 17)]));
+      conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[51] * kernel_shared[((((int)threadIdx.x) * 24) + 17)]));
+      conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[52] * kernel_shared[((((int)threadIdx.x) * 24) + 17)]));
+      conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[53] * kernel_shared[((((int)threadIdx.x) * 24) + 17)]));
+      conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[47] * kernel_shared[((((int)threadIdx.x) * 24) + 209)]));
+      conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[48] * kernel_shared[((((int)threadIdx.x) * 24) + 209)]));
+      conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[49] * kernel_shared[((((int)threadIdx.x) * 24) + 209)]));
+      conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[50] * kernel_shared[((((int)threadIdx.x) * 24) + 209)]));
+      conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[51] * kernel_shared[((((int)threadIdx.x) * 24) + 209)]));
+      conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[52] * kernel_shared[((((int)threadIdx.x) * 24) + 209)]));
+      conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[53] * kernel_shared[((((int)threadIdx.x) * 24) + 209)]));
+      conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[54] * kernel_shared[((((int)threadIdx.x) * 24) + 18)]));
+      conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[55] * kernel_shared[((((int)threadIdx.x) * 24) + 18)]));
+      conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[56] * kernel_shared[((((int)threadIdx.x) * 24) + 18)]));
+      conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[57] * kernel_shared[((((int)threadIdx.x) * 24) + 18)]));
+      conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[58] * kernel_shared[((((int)threadIdx.x) * 24) + 18)]));
+      conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[59] * kernel_shared[((((int)threadIdx.x) * 24) + 18)]));
+      conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[60] * kernel_shared[((((int)threadIdx.x) * 24) + 18)]));
+      conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[54] * kernel_shared[((((int)threadIdx.x) * 24) + 210)]));
+      conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[55] * kernel_shared[((((int)threadIdx.x) * 24) + 210)]));
+      conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[56] * kernel_shared[((((int)threadIdx.x) * 24) + 210)]));
+      conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[57] * kernel_shared[((((int)threadIdx.x) * 24) + 210)]));
+      conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[58] * kernel_shared[((((int)threadIdx.x) * 24) + 210)]));
+      conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[59] * kernel_shared[((((int)threadIdx.x) * 24) + 210)]));
+      conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[60] * kernel_shared[((((int)threadIdx.x) * 24) + 210)]));
+      conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[55] * kernel_shared[((((int)threadIdx.x) * 24) + 19)]));
+      conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[56] * kernel_shared[((((int)threadIdx.x) * 24) + 19)]));
+      conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[57] * kernel_shared[((((int)threadIdx.x) * 24) + 19)]));
+      conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[58] * kernel_shared[((((int)threadIdx.x) * 24) + 19)]));
+      conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[59] * kernel_shared[((((int)threadIdx.x) * 24) + 19)]));
+      conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[60] * kernel_shared[((((int)threadIdx.x) * 24) + 19)]));
+      conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[61] * kernel_shared[((((int)threadIdx.x) * 24) + 19)]));
+      conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[55] * kernel_shared[((((int)threadIdx.x) * 24) + 211)]));
+      conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[56] * kernel_shared[((((int)threadIdx.x) * 24) + 211)]));
+      conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[57] * kernel_shared[((((int)threadIdx.x) * 24) + 211)]));
+      conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[58] * kernel_shared[((((int)threadIdx.x) * 24) + 211)]));
+      conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[59] * kernel_shared[((((int)threadIdx.x) * 24) + 211)]));
+      conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[60] * kernel_shared[((((int)threadIdx.x) * 24) + 211)]));
+      conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[61] * kernel_shared[((((int)threadIdx.x) * 24) + 211)]));
+      conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[56] * kernel_shared[((((int)threadIdx.x) * 24) + 20)]));
+      conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[57] * kernel_shared[((((int)threadIdx.x) * 24) + 20)]));
+      conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[58] * kernel_shared[((((int)threadIdx.x) * 24) + 20)]));
+      conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[59] * kernel_shared[((((int)threadIdx.x) * 24) + 20)]));
+      conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[60] * kernel_shared[((((int)threadIdx.x) * 24) + 20)]));
+      conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[61] * kernel_shared[((((int)threadIdx.x) * 24) + 20)]));
+      conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[62] * kernel_shared[((((int)threadIdx.x) * 24) + 20)]));
+      conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[56] * kernel_shared[((((int)threadIdx.x) * 24) + 212)]));
+      conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[57] * kernel_shared[((((int)threadIdx.x) * 24) + 212)]));
+      conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[58] * kernel_shared[((((int)threadIdx.x) * 24) + 212)]));
+      conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[59] * kernel_shared[((((int)threadIdx.x) * 24) + 212)]));
+      conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[60] * kernel_shared[((((int)threadIdx.x) * 24) + 212)]));
+      conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[61] * kernel_shared[((((int)threadIdx.x) * 24) + 212)]));
+      conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[62] * kernel_shared[((((int)threadIdx.x) * 24) + 212)]));
+      conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[63] * kernel_shared[((((int)threadIdx.x) * 24) + 21)]));
+      conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[64] * kernel_shared[((((int)threadIdx.x) * 24) + 21)]));
+      conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[65] * kernel_shared[((((int)threadIdx.x) * 24) + 21)]));
+      conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[66] * kernel_shared[((((int)threadIdx.x) * 24) + 21)]));
+      conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[67] * kernel_shared[((((int)threadIdx.x) * 24) + 21)]));
+      conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[68] * kernel_shared[((((int)threadIdx.x) * 24) + 21)]));
+      conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[69] * kernel_shared[((((int)threadIdx.x) * 24) + 21)]));
+      conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[63] * kernel_shared[((((int)threadIdx.x) * 24) + 213)]));
+      conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[64] * kernel_shared[((((int)threadIdx.x) * 24) + 213)]));
+      conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[65] * kernel_shared[((((int)threadIdx.x) * 24) + 213)]));
+      conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[66] * kernel_shared[((((int)threadIdx.x) * 24) + 213)]));
+      conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[67] * kernel_shared[((((int)threadIdx.x) * 24) + 213)]));
+      conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[68] * kernel_shared[((((int)threadIdx.x) * 24) + 213)]));
+      conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[69] * kernel_shared[((((int)threadIdx.x) * 24) + 213)]));
+      conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[64] * kernel_shared[((((int)threadIdx.x) * 24) + 22)]));
+      conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[65] * kernel_shared[((((int)threadIdx.x) * 24) + 22)]));
+      conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[66] * kernel_shared[((((int)threadIdx.x) * 24) + 22)]));
+      conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[67] * kernel_shared[((((int)threadIdx.x) * 24) + 22)]));
+      conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[68] * kernel_shared[((((int)threadIdx.x) * 24) + 22)]));
+      conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[69] * kernel_shared[((((int)threadIdx.x) * 24) + 22)]));
+      conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[70] * kernel_shared[((((int)threadIdx.x) * 24) + 22)]));
+      conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[64] * kernel_shared[((((int)threadIdx.x) * 24) + 214)]));
+      conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[65] * kernel_shared[((((int)threadIdx.x) * 24) + 214)]));
+      conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[66] * kernel_shared[((((int)threadIdx.x) * 24) + 214)]));
+      conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[67] * kernel_shared[((((int)threadIdx.x) * 24) + 214)]));
+      conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[68] * kernel_shared[((((int)threadIdx.x) * 24) + 214)]));
+      conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[69] * kernel_shared[((((int)threadIdx.x) * 24) + 214)]));
+      conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[70] * kernel_shared[((((int)threadIdx.x) * 24) + 214)]));
+      conv2d_nchw[0] = (conv2d_nchw[0] + (pad_temp_shared[65] * kernel_shared[((((int)threadIdx.x) * 24) + 23)]));
+      conv2d_nchw[1] = (conv2d_nchw[1] + (pad_temp_shared[66] * kernel_shared[((((int)threadIdx.x) * 24) + 23)]));
+      conv2d_nchw[2] = (conv2d_nchw[2] + (pad_temp_shared[67] * kernel_shared[((((int)threadIdx.x) * 24) + 23)]));
+      conv2d_nchw[3] = (conv2d_nchw[3] + (pad_temp_shared[68] * kernel_shared[((((int)threadIdx.x) * 24) + 23)]));
+      conv2d_nchw[4] = (conv2d_nchw[4] + (pad_temp_shared[69] * kernel_shared[((((int)threadIdx.x) * 24) + 23)]));
+      conv2d_nchw[5] = (conv2d_nchw[5] + (pad_temp_shared[70] * kernel_shared[((((int)threadIdx.x) * 24) + 23)]));
+      conv2d_nchw[6] = (conv2d_nchw[6] + (pad_temp_shared[71] * kernel_shared[((((int)threadIdx.x) * 24) + 23)]));
+      conv2d_nchw[7] = (conv2d_nchw[7] + (pad_temp_shared[65] * kernel_shared[((((int)threadIdx.x) * 24) + 215)]));
+      conv2d_nchw[8] = (conv2d_nchw[8] + (pad_temp_shared[66] * kernel_shared[((((int)threadIdx.x) * 24) + 215)]));
+      conv2d_nchw[9] = (conv2d_nchw[9] + (pad_temp_shared[67] * kernel_shared[((((int)threadIdx.x) * 24) + 215)]));
+      conv2d_nchw[10] = (conv2d_nchw[10] + (pad_temp_shared[68] * kernel_shared[((((int)threadIdx.x) * 24) + 215)]));
+      conv2d_nchw[11] = (conv2d_nchw[11] + (pad_temp_shared[69] * kernel_shared[((((int)threadIdx.x) * 24) + 215)]));
+      conv2d_nchw[12] = (conv2d_nchw[12] + (pad_temp_shared[70] * kernel_shared[((((int)threadIdx.x) * 24) + 215)]));
+      conv2d_nchw[13] = (conv2d_nchw[13] + (pad_temp_shared[71] * kernel_shared[((((int)threadIdx.x) * 24) + 215)]));
     }
   }
-  for (int i1_inner = 0; i1_inner &lt; 4; ++i1_inner) {
-    compute[((((((int)blockIdx.x) * 784) + ((((int)threadIdx.x) / 7) * 196)) + (i1_inner * 49)) + (((int)threadIdx.x) % 7))] = max((conv2d_nchw[i1_inner] + bias[(((((int)blockIdx.x) * 16) + ((((int)threadIdx.x) / 7) * 4)) + i1_inner)]), 0.000000e+00f);
-    compute[(((((((int)blockIdx.x) * 784) + ((((int)threadIdx.x) / 7) * 196)) + (i1_inner * 49)) + (((int)threadIdx.x) % 7)) + 7)] = max((conv2d_nchw[(i1_inner + 4)] + bias[(((((int)blockIdx.x) * 16) + ((((int)threadIdx.x) / 7) * 4)) + i1_inner)]), 0.000000e+00f);
-    compute[(((((((int)blockIdx.x) * 784) + ((((int)threadIdx.x) / 7) * 196)) + (i1_inner * 49)) + (((int)threadIdx.x) % 7)) + 14)] = max((conv2d_nchw[(i1_inner + 8)] + bias[(((((int)blockIdx.x) * 16) + ((((int)threadIdx.x) / 7) * 4)) + i1_inner)]), 0.000000e+00f);
-    compute[(((((((int)blockIdx.x) * 784) + ((((int)threadIdx.x) / 7) * 196)) + (i1_inner * 49)) + (((int)threadIdx.x) % 7)) + 21)] = max((conv2d_nchw[(i1_inner + 12)] + bias[(((((int)blockIdx.x) * 16) + ((((int)threadIdx.x) / 7) * 4)) + i1_inner)]), 0.000000e+00f);
-    compute[(((((((int)blockIdx.x) * 784) + ((((int)threadIdx.x) / 7) * 196)) + (i1_inner * 49)) + (((int)threadIdx.x) % 7)) + 28)] = max((conv2d_nchw[(i1_inner + 16)] + bias[(((((int)blockIdx.x) * 16) + ((((int)threadIdx.x) / 7) * 4)) + i1_inner)]), 0.000000e+00f);
-    compute[(((((((int)blockIdx.x) * 784) + ((((int)threadIdx.x) / 7) * 196)) + (i1_inner * 49)) + (((int)threadIdx.x) % 7)) + 35)] = max((conv2d_nchw[(i1_inner + 20)] + bias[(((((int)blockIdx.x) * 16) + ((((int)threadIdx.x) / 7) * 4)) + i1_inner)]), 0.000000e+00f);
-    compute[(((((((int)blockIdx.x) * 784) + ((((int)threadIdx.x) / 7) * 196)) + (i1_inner * 49)) + (((int)threadIdx.x) % 7)) + 42)] = max((conv2d_nchw[(i1_inner + 24)] + bias[(((((int)blockIdx.x) * 16) + ((((int)threadIdx.x) / 7) * 4)) + i1_inner)]), 0.000000e+00f);
-  }
+  compute[((((((int)blockIdx.x) / 7) * 784) + (((int)threadIdx.x) * 49)) + ((((int)blockIdx.x) % 7) * 7))] = max((conv2d_nchw[0] + bias[(((((int)blockIdx.x) / 7) * 16) + ((int)threadIdx.x))]), 0.000000e+00f);
+  compute[(((((((int)blockIdx.x) / 7) * 784) + (((int)threadIdx.x) * 49)) + ((((int)blockIdx.x) % 7) * 7)) + 1)] = max((conv2d_nchw[1] + bias[(((((int)blockIdx.x) / 7) * 16) + ((int)threadIdx.x))]), 0.000000e+00f);
+  compute[(((((((int)blockIdx.x) / 7) * 784) + (((int)threadIdx.x) * 49)) + ((((int)blockIdx.x) % 7) * 7)) + 2)] = max((conv2d_nchw[2] + bias[(((((int)blockIdx.x) / 7) * 16) + ((int)threadIdx.x))]), 0.000000e+00f);
+  compute[(((((((int)blockIdx.x) / 7) * 784) + (((int)threadIdx.x) * 49)) + ((((int)blockIdx.x) % 7) * 7)) + 3)] = max((conv2d_nchw[3] + bias[(((((int)blockIdx.x) / 7) * 16) + ((int)threadIdx.x))]), 0.000000e+00f);
+  compute[(((((((int)blockIdx.x) / 7) * 784) + (((int)threadIdx.x) * 49)) + ((((int)blockIdx.x) % 7) * 7)) + 4)] = max((conv2d_nchw[4] + bias[(((((int)blockIdx.x) / 7) * 16) + ((int)threadIdx.x))]), 0.000000e+00f);
+  compute[(((((((int)blockIdx.x) / 7) * 784) + (((int)threadIdx.x) * 49)) + ((((int)blockIdx.x) % 7) * 7)) + 5)] = max((conv2d_nchw[5] + bias[(((((int)blockIdx.x) / 7) * 16) + ((int)threadIdx.x))]), 0.000000e+00f);
+  compute[(((((((int)blockIdx.x) / 7) * 784) + (((int)threadIdx.x) * 49)) + ((((int)blockIdx.x) % 7) * 7)) + 6)] = max((conv2d_nchw[6] + bias[(((((int)blockIdx.x) / 7) * 16) + ((int)threadIdx.x))]), 0.000000e+00f);
+  compute[(((((((int)blockIdx.x) / 7) * 784) + (((int)threadIdx.x) * 49)) + ((((int)blockIdx.x) % 7) * 7)) + 392)] = max((conv2d_nchw[7] + bias[((((((int)blockIdx.x) / 7) * 16) + ((int)threadIdx.x)) + 8)]), 0.000000e+00f);
+  compute[(((((((int)blockIdx.x) / 7) * 784) + (((int)threadIdx.x) * 49)) + ((((int)blockIdx.x) % 7) * 7)) + 393)] = max((conv2d_nchw[8] + bias[((((((int)blockIdx.x) / 7) * 16) + ((int)threadIdx.x)) + 8)]), 0.000000e+00f);
+  compute[(((((((int)blockIdx.x) / 7) * 784) + (((int)threadIdx.x) * 49)) + ((((int)blockIdx.x) % 7) * 7)) + 394)] = max((conv2d_nchw[9] + bias[((((((int)blockIdx.x) / 7) * 16) + ((int)threadIdx.x)) + 8)]), 0.000000e+00f);
+  compute[(((((((int)blockIdx.x) / 7) * 784) + (((int)threadIdx.x) * 49)) + ((((int)blockIdx.x) % 7) * 7)) + 395)] = max((conv2d_nchw[10] + bias[((((((int)blockIdx.x) / 7) * 16) + ((int)threadIdx.x)) + 8)]), 0.000000e+00f);
+  compute[(((((((int)blockIdx.x) / 7) * 784) + (((int)threadIdx.x) * 49)) + ((((int)blockIdx.x) % 7) * 7)) + 396)] = max((conv2d_nchw[11] + bias[((((((int)blockIdx.x) / 7) * 16) + ((int)threadIdx.x)) + 8)]), 0.000000e+00f);
+  compute[(((((((int)blockIdx.x) / 7) * 784) + (((int)threadIdx.x) * 49)) + ((((int)blockIdx.x) % 7) * 7)) + 397)] = max((conv2d_nchw[12] + bias[((((((int)blockIdx.x) / 7) * 16) + ((int)threadIdx.x)) + 8)]), 0.000000e+00f);
+  compute[(((((((int)blockIdx.x) / 7) * 784) + (((int)threadIdx.x) * 49)) + ((((int)blockIdx.x) % 7) * 7)) + 398)] = max((conv2d_nchw[13] + bias[((((((int)blockIdx.x) / 7) * 16) + ((int)threadIdx.x)) + 8)]), 0.000000e+00f);
 }
 </pre></div>
 </div>
@@ -1296,7 +1568,7 @@ In the example below we resume the status and do more 5 trials.</p>
 Get devices for measurement successfully!
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes  20.154 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes  21.500 seconds)</p>
 <div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-tune-with-autoscheduler-tune-conv2d-layer-cuda-py">
 <div class="sphx-glr-download docutils container">
 <p><a class="reference download internal" download="" href="../../_downloads/e3e540f3b477c0c52d8eb73e674e8ffd/tune_conv2d_layer_cuda.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">tune_conv2d_layer_cuda.py</span></code></a></p>
diff --git a/docs/how_to/tune_with_autoscheduler/tune_network_cuda.html b/docs/how_to/tune_with_autoscheduler/tune_network_cuda.html
index 1ca7f93fb..364e534b2 100644
--- a/docs/how_to/tune_with_autoscheduler/tune_network_cuda.html
+++ b/docs/how_to/tune_with_autoscheduler/tune_network_cuda.html
@@ -876,7 +876,7 @@ so we can read the log file and load the best schedules.</p>
 Evaluate inference time cost...
 Execution time summary:
  mean (ms)   median (ms)    max (ms)     min (ms)     std (ms)
-   9.8233       9.8322       9.8639       9.7739       0.0372
+   9.8680       9.8876       9.9006       9.8159       0.0372
 </pre></div>
 </div>
 </div>
diff --git a/docs/how_to/tune_with_autoscheduler/tune_network_x86.html b/docs/how_to/tune_with_autoscheduler/tune_network_x86.html
index 16fa477ad..5e2896d8e 100644
--- a/docs/how_to/tune_with_autoscheduler/tune_network_x86.html
+++ b/docs/how_to/tune_with_autoscheduler/tune_network_x86.html
@@ -895,7 +895,7 @@ so we can read the log file and load the best schedules.</p>
 Evaluate inference time cost...
 Execution time summary:
  mean (ms)   median (ms)    max (ms)     min (ms)     std (ms)
-  755.6295     751.7929     764.4012     750.6943      6.2188
+  769.9250     770.6566     774.9276     764.1909      4.4136
 </pre></div>
 </div>
 </div>
@@ -917,7 +917,7 @@ to learn how to use the RPC Tracker and RPC Server.
 To use the RPC Tracker in auto-scheduler, replace the runner in <code class="code docutils literal notranslate"><span class="pre">TuningOptions</span></code>
 with <a class="reference internal" href="../../reference/api/python/auto_scheduler.html#tvm.auto_scheduler.RPCRunner" title="tvm.auto_scheduler.RPCRunner"><code class="xref any py py-class docutils literal notranslate"><span class="pre">auto_scheduler.RPCRunner</span></code></a>.</p></li>
 </ol>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes  18.957 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes  21.317 seconds)</p>
 <div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-tune-with-autoscheduler-tune-network-x86-py">
 <div class="sphx-glr-download docutils container">
 <p><a class="reference download internal" download="" href="../../_downloads/e416b94ca1090b0897c0f6e0df95b911/tune_network_x86.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">tune_network_x86.py</span></code></a></p>
diff --git a/docs/how_to/tune_with_autoscheduler/tune_sparse_x86.html b/docs/how_to/tune_with_autoscheduler/tune_sparse_x86.html
index e85558411..e3f52de4e 100644
--- a/docs/how_to/tune_with_autoscheduler/tune_sparse_x86.html
+++ b/docs/how_to/tune_with_autoscheduler/tune_sparse_x86.html
@@ -600,407 +600,77 @@ layout transformation, parallelization, vectorization, unrolling, and operator f
              placeholder_4: Buffer(placeholder_14: Pointer(float32), float32, [65536], []),
              compute: Buffer(compute_2: Pointer(float32), float32, [65536], [])}
   buffer_map = {placeholder_5: placeholder, placeholder_6: placeholder_1, placeholder_7: placeholder_2, placeholder_8: placeholder_3, placeholder_9: placeholder_4, compute_1: compute} {
-  for (i0.outer: int32, 0, 16) &quot;parallel&quot; {
-    allocate(compute_3: Pointer(global float32), float32, [256]), storage_scope = global;
-    for (i1.outer: int32, 0, 16) {
-      for (nb_j.inner: int32, 0, 2) {
-        let cse_var_2: int32 = (nb_j.inner*16)
-        let cse_var_1: int32 = ((i1.outer*2) + nb_j.inner)
-         {
-          compute_4: Buffer(compute_3, float32, [256], [])[cse_var_2] = 0f32
-          compute_4[(cse_var_2 + 1)] = 0f32
-          compute_4[(cse_var_2 + 2)] = 0f32
-          compute_4[(cse_var_2 + 3)] = 0f32
-          compute_4[(cse_var_2 + 4)] = 0f32
-          compute_4[(cse_var_2 + 5)] = 0f32
-          compute_4[(cse_var_2 + 6)] = 0f32
-          compute_4[(cse_var_2 + 7)] = 0f32
-          compute_4[(cse_var_2 + 8)] = 0f32
-          compute_4[(cse_var_2 + 9)] = 0f32
-          compute_4[(cse_var_2 + 10)] = 0f32
-          compute_4[(cse_var_2 + 11)] = 0f32
-          compute_4[(cse_var_2 + 12)] = 0f32
-          compute_4[(cse_var_2 + 13)] = 0f32
-          compute_4[(cse_var_2 + 14)] = 0f32
-          compute_4[(cse_var_2 + 15)] = 0f32
-          compute_4[(cse_var_2 + 32)] = 0f32
-          compute_4[(cse_var_2 + 33)] = 0f32
-          compute_4[(cse_var_2 + 34)] = 0f32
-          compute_4[(cse_var_2 + 35)] = 0f32
-          compute_4[(cse_var_2 + 36)] = 0f32
-          compute_4[(cse_var_2 + 37)] = 0f32
-          compute_4[(cse_var_2 + 38)] = 0f32
-          compute_4[(cse_var_2 + 39)] = 0f32
-          compute_4[(cse_var_2 + 40)] = 0f32
-          compute_4[(cse_var_2 + 41)] = 0f32
-          compute_4[(cse_var_2 + 42)] = 0f32
-          compute_4[(cse_var_2 + 43)] = 0f32
-          compute_4[(cse_var_2 + 44)] = 0f32
-          compute_4[(cse_var_2 + 45)] = 0f32
-          compute_4[(cse_var_2 + 46)] = 0f32
-          compute_4[(cse_var_2 + 47)] = 0f32
-          compute_4[(cse_var_2 + 64)] = 0f32
-          compute_4[(cse_var_2 + 65)] = 0f32
-          compute_4[(cse_var_2 + 66)] = 0f32
-          compute_4[(cse_var_2 + 67)] = 0f32
-          compute_4[(cse_var_2 + 68)] = 0f32
-          compute_4[(cse_var_2 + 69)] = 0f32
-          compute_4[(cse_var_2 + 70)] = 0f32
-          compute_4[(cse_var_2 + 71)] = 0f32
-          compute_4[(cse_var_2 + 72)] = 0f32
-          compute_4[(cse_var_2 + 73)] = 0f32
-          compute_4[(cse_var_2 + 74)] = 0f32
-          compute_4[(cse_var_2 + 75)] = 0f32
-          compute_4[(cse_var_2 + 76)] = 0f32
-          compute_4[(cse_var_2 + 77)] = 0f32
-          compute_4[(cse_var_2 + 78)] = 0f32
-          compute_4[(cse_var_2 + 79)] = 0f32
-          compute_4[(cse_var_2 + 96)] = 0f32
-          compute_4[(cse_var_2 + 97)] = 0f32
-          compute_4[(cse_var_2 + 98)] = 0f32
-          compute_4[(cse_var_2 + 99)] = 0f32
-          compute_4[(cse_var_2 + 100)] = 0f32
-          compute_4[(cse_var_2 + 101)] = 0f32
-          compute_4[(cse_var_2 + 102)] = 0f32
-          compute_4[(cse_var_2 + 103)] = 0f32
-          compute_4[(cse_var_2 + 104)] = 0f32
-          compute_4[(cse_var_2 + 105)] = 0f32
-          compute_4[(cse_var_2 + 106)] = 0f32
-          compute_4[(cse_var_2 + 107)] = 0f32
-          compute_4[(cse_var_2 + 108)] = 0f32
-          compute_4[(cse_var_2 + 109)] = 0f32
-          compute_4[(cse_var_2 + 110)] = 0f32
-          compute_4[(cse_var_2 + 111)] = 0f32
-          compute_4[(cse_var_2 + 128)] = 0f32
-          compute_4[(cse_var_2 + 129)] = 0f32
-          compute_4[(cse_var_2 + 130)] = 0f32
-          compute_4[(cse_var_2 + 131)] = 0f32
-          compute_4[(cse_var_2 + 132)] = 0f32
-          compute_4[(cse_var_2 + 133)] = 0f32
-          compute_4[(cse_var_2 + 134)] = 0f32
-          compute_4[(cse_var_2 + 135)] = 0f32
-          compute_4[(cse_var_2 + 136)] = 0f32
-          compute_4[(cse_var_2 + 137)] = 0f32
-          compute_4[(cse_var_2 + 138)] = 0f32
-          compute_4[(cse_var_2 + 139)] = 0f32
-          compute_4[(cse_var_2 + 140)] = 0f32
-          compute_4[(cse_var_2 + 141)] = 0f32
-          compute_4[(cse_var_2 + 142)] = 0f32
-          compute_4[(cse_var_2 + 143)] = 0f32
-          compute_4[(cse_var_2 + 160)] = 0f32
-          compute_4[(cse_var_2 + 161)] = 0f32
-          compute_4[(cse_var_2 + 162)] = 0f32
-          compute_4[(cse_var_2 + 163)] = 0f32
-          compute_4[(cse_var_2 + 164)] = 0f32
-          compute_4[(cse_var_2 + 165)] = 0f32
-          compute_4[(cse_var_2 + 166)] = 0f32
-          compute_4[(cse_var_2 + 167)] = 0f32
-          compute_4[(cse_var_2 + 168)] = 0f32
-          compute_4[(cse_var_2 + 169)] = 0f32
-          compute_4[(cse_var_2 + 170)] = 0f32
-          compute_4[(cse_var_2 + 171)] = 0f32
-          compute_4[(cse_var_2 + 172)] = 0f32
-          compute_4[(cse_var_2 + 173)] = 0f32
-          compute_4[(cse_var_2 + 174)] = 0f32
-          compute_4[(cse_var_2 + 175)] = 0f32
-          compute_4[(cse_var_2 + 192)] = 0f32
-          compute_4[(cse_var_2 + 193)] = 0f32
-          compute_4[(cse_var_2 + 194)] = 0f32
-          compute_4[(cse_var_2 + 195)] = 0f32
-          compute_4[(cse_var_2 + 196)] = 0f32
-          compute_4[(cse_var_2 + 197)] = 0f32
-          compute_4[(cse_var_2 + 198)] = 0f32
-          compute_4[(cse_var_2 + 199)] = 0f32
-          compute_4[(cse_var_2 + 200)] = 0f32
-          compute_4[(cse_var_2 + 201)] = 0f32
-          compute_4[(cse_var_2 + 202)] = 0f32
-          compute_4[(cse_var_2 + 203)] = 0f32
-          compute_4[(cse_var_2 + 204)] = 0f32
-          compute_4[(cse_var_2 + 205)] = 0f32
-          compute_4[(cse_var_2 + 206)] = 0f32
-          compute_4[(cse_var_2 + 207)] = 0f32
-          compute_4[(cse_var_2 + 224)] = 0f32
-          compute_4[(cse_var_2 + 225)] = 0f32
-          compute_4[(cse_var_2 + 226)] = 0f32
-          compute_4[(cse_var_2 + 227)] = 0f32
-          compute_4[(cse_var_2 + 228)] = 0f32
-          compute_4[(cse_var_2 + 229)] = 0f32
-          compute_4[(cse_var_2 + 230)] = 0f32
-          compute_4[(cse_var_2 + 231)] = 0f32
-          compute_4[(cse_var_2 + 232)] = 0f32
-          compute_4[(cse_var_2 + 233)] = 0f32
-          compute_4[(cse_var_2 + 234)] = 0f32
-          compute_4[(cse_var_2 + 235)] = 0f32
-          compute_4[(cse_var_2 + 236)] = 0f32
-          compute_4[(cse_var_2 + 237)] = 0f32
-          compute_4[(cse_var_2 + 238)] = 0f32
-          compute_4[(cse_var_2 + 239)] = 0f32
-          for (elem_idx: int32, 0, (placeholder_3[(cse_var_1 + 1)] - placeholder_3[cse_var_1])) {
-            let cse_var_131: int32 = (cse_var_2 + 143)
-            let cse_var_130: int32 = (cse_var_2 + 15)
-            let cse_var_129: int32 = (cse_var_2 + 160)
-            let cse_var_128: int32 = (cse_var_2 + 161)
-            let cse_var_127: int32 = (cse_var_2 + 162)
-            let cse_var_126: int32 = (cse_var_2 + 163)
-            let cse_var_125: int32 = (cse_var_2 + 164)
-            let cse_var_124: int32 = (cse_var_2 + 165)
-            let cse_var_123: int32 = (cse_var_2 + 166)
-            let cse_var_122: int32 = (cse_var_2 + 167)
-            let cse_var_121: int32 = (cse_var_2 + 168)
-            let cse_var_120: int32 = (cse_var_2 + 169)
-            let cse_var_119: int32 = (cse_var_2 + 170)
-            let cse_var_118: int32 = (cse_var_2 + 171)
-            let cse_var_117: int32 = (cse_var_2 + 172)
-            let cse_var_116: int32 = (cse_var_2 + 1)
-            let cse_var_115: int32 = (cse_var_2 + 174)
-            let cse_var_114: int32 = (cse_var_2 + 175)
-            let cse_var_113: int32 = (cse_var_2 + 192)
-            let cse_var_112: int32 = (cse_var_2 + 193)
-            let cse_var_111: int32 = (cse_var_2 + 194)
-            let cse_var_110: int32 = (cse_var_2 + 195)
-            let cse_var_109: int32 = (cse_var_2 + 196)
-            let cse_var_108: int32 = (cse_var_2 + 197)
-            let cse_var_107: int32 = (cse_var_2 + 198)
-            let cse_var_106: int32 = (cse_var_2 + 199)
-            let cse_var_105: int32 = (cse_var_2 + 2)
-            let cse_var_104: int32 = (cse_var_2 + 200)
-            let cse_var_103: int32 = (cse_var_2 + 201)
-            let cse_var_102: int32 = (cse_var_2 + 202)
-            let cse_var_101: int32 = (cse_var_2 + 203)
-            let cse_var_100: int32 = (cse_var_2 + 173)
-            let cse_var_99: int32 = (cse_var_2 + 10)
-            let cse_var_98: int32 = (cse_var_2 + 100)
-            let cse_var_97: int32 = (cse_var_2 + 101)
-            let cse_var_96: int32 = (cse_var_2 + 102)
-            let cse_var_95: int32 = (cse_var_2 + 103)
-            let cse_var_94: int32 = (cse_var_2 + 104)
-            let cse_var_93: int32 = (cse_var_2 + 105)
-            let cse_var_92: int32 = (cse_var_2 + 106)
-            let cse_var_91: int32 = (cse_var_2 + 107)
-            let cse_var_90: int32 = (cse_var_2 + 108)
-            let cse_var_89: int32 = (cse_var_2 + 109)
-            let cse_var_88: int32 = (cse_var_2 + 11)
-            let cse_var_87: int32 = (cse_var_2 + 110)
-            let cse_var_86: int32 = (cse_var_2 + 111)
-            let cse_var_85: int32 = (cse_var_2 + 12)
-            let cse_var_84: int32 = (cse_var_2 + 142)
-            let cse_var_83: int32 = (cse_var_2 + 129)
-            let cse_var_82: int32 = (cse_var_2 + 13)
-            let cse_var_81: int32 = (cse_var_2 + 130)
-            let cse_var_80: int32 = (cse_var_2 + 131)
-            let cse_var_79: int32 = (cse_var_2 + 132)
-            let cse_var_78: int32 = (cse_var_2 + 133)
-            let cse_var_77: int32 = (cse_var_2 + 134)
-            let cse_var_76: int32 = (cse_var_2 + 135)
-            let cse_var_75: int32 = (cse_var_2 + 136)
-            let cse_var_74: int32 = (cse_var_2 + 137)
-            let cse_var_73: int32 = (cse_var_2 + 138)
-            let cse_var_72: int32 = (cse_var_2 + 139)
-            let cse_var_71: int32 = (cse_var_2 + 14)
-            let cse_var_70: int32 = (cse_var_2 + 140)
-            let cse_var_69: int32 = (cse_var_2 + 141)
-            let cse_var_68: int32 = (cse_var_2 + 128)
-            let cse_var_67: int32 = (cse_var_2 + 44)
-            let cse_var_66: int32 = (cse_var_2 + 45)
-            let cse_var_65: int32 = (cse_var_2 + 46)
-            let cse_var_64: int32 = (cse_var_2 + 47)
-            let cse_var_63: int32 = (cse_var_2 + 5)
-            let cse_var_62: int32 = (cse_var_2 + 6)
-            let cse_var_61: int32 = (cse_var_2 + 64)
-            let cse_var_60: int32 = (cse_var_2 + 65)
-            let cse_var_59: int32 = (cse_var_2 + 66)
-            let cse_var_58: int32 = (cse_var_2 + 67)
-            let cse_var_57: int32 = (cse_var_2 + 68)
-            let cse_var_56: int32 = (cse_var_2 + 69)
-            let cse_var_55: int32 = (cse_var_2 + 7)
-            let cse_var_54: int32 = (cse_var_2 + 70)
-            let cse_var_53: int32 = (cse_var_2 + 71)
-            let cse_var_52: int32 = (cse_var_2 + 204)
-            let cse_var_51: int32 = (cse_var_2 + 73)
-            let cse_var_50: int32 = (cse_var_2 + 74)
-            let cse_var_49: int32 = (cse_var_2 + 75)
-            let cse_var_48: int32 = (cse_var_2 + 76)
-            let cse_var_47: int32 = (cse_var_2 + 77)
-            let cse_var_46: int32 = (cse_var_2 + 78)
-            let cse_var_45: int32 = (cse_var_2 + 79)
-            let cse_var_44: int32 = (cse_var_2 + 8)
-            let cse_var_43: int32 = (cse_var_2 + 9)
-            let cse_var_42: int32 = (cse_var_2 + 96)
-            let cse_var_41: int32 = (cse_var_2 + 97)
-            let cse_var_40: int32 = (cse_var_2 + 98)
-            let cse_var_39: int32 = (cse_var_2 + 99)
-            let cse_var_38: int32 = (elem_idx*16)
-            let cse_var_37: int32 = (i0.outer*2048)
-            let cse_var_36: int32 = (cse_var_2 + 72)
-            let cse_var_35: int32 = (cse_var_2 + 205)
-            let cse_var_34: int32 = (cse_var_2 + 206)
-            let cse_var_33: int32 = (cse_var_2 + 207)
-            let cse_var_32: int32 = (cse_var_2 + 224)
-            let cse_var_31: int32 = (cse_var_2 + 225)
-            let cse_var_30: int32 = (cse_var_2 + 226)
-            let cse_var_29: int32 = (cse_var_2 + 227)
-            let cse_var_28: int32 = (cse_var_2 + 228)
-            let cse_var_27: int32 = (cse_var_2 + 229)
-            let cse_var_26: int32 = (cse_var_2 + 230)
-            let cse_var_25: int32 = (cse_var_2 + 231)
-            let cse_var_24: int32 = (cse_var_2 + 232)
-            let cse_var_23: int32 = (cse_var_2 + 233)
-            let cse_var_22: int32 = (cse_var_2 + 234)
-            let cse_var_21: int32 = (cse_var_2 + 235)
-            let cse_var_20: int32 = (cse_var_2 + 43)
-            let cse_var_19: int32 = (cse_var_2 + 42)
-            let cse_var_18: int32 = (cse_var_2 + 41)
-            let cse_var_17: int32 = (cse_var_2 + 40)
-            let cse_var_16: int32 = (cse_var_2 + 4)
-            let cse_var_15: int32 = (cse_var_2 + 39)
-            let cse_var_14: int32 = (cse_var_2 + 38)
-            let cse_var_13: int32 = (cse_var_2 + 37)
-            let cse_var_12: int32 = (cse_var_2 + 236)
-            let cse_var_11: int32 = (cse_var_2 + 35)
-            let cse_var_10: int32 = (cse_var_2 + 34)
-            let cse_var_9: int32 = (cse_var_2 + 33)
-            let cse_var_8: int32 = (cse_var_2 + 32)
-            let cse_var_7: int32 = (cse_var_2 + 3)
-            let cse_var_6: int32 = (cse_var_2 + 239)
-            let cse_var_5: int32 = (cse_var_2 + 238)
-            let cse_var_4: int32 = (cse_var_2 + 237)
-            let cse_var_3: int32 = (cse_var_2 + 36)
+  for (i0.outer.i1.outer.fused: int32, 0, 32) &quot;parallel&quot; {
+    allocate(compute_3: Pointer(global float32), float32, [2048]), storage_scope = global {
+      for (i.outer.inner: int32, 0, 4) {
+        for (nb_j.inner: int32, 0, 2) {
+          for (i.inner.init: int32, 0, 16) {
+            let cse_var_1: int32 = (((i.outer.inner*512) + (i.inner.init*32)) + (nb_j.inner*16))
              {
-              compute_4[cse_var_2] = (compute_4[cse_var_2] + (placeholder_1[((placeholder_3[cse_var_1]*16) + cse_var_38)]*max(placeholder[(cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)])], 0f32)))
-              compute_4[cse_var_116] = (compute_4[cse_var_116] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 1)]*max(placeholder[(cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)])], 0f32)))
-              compute_4[cse_var_105] = (compute_4[cse_var_105] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 2)]*max(placeholder[(cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)])], 0f32)))
-              compute_4[cse_var_7] = (compute_4[cse_var_7] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 3)]*max(placeholder[(cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)])], 0f32)))
-              compute_4[cse_var_16] = (compute_4[cse_var_16] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 4)]*max(placeholder[(cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)])], 0f32)))
-              compute_4[cse_var_63] = (compute_4[cse_var_63] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 5)]*max(placeholder[(cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)])], 0f32)))
-              compute_4[cse_var_62] = (compute_4[cse_var_62] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 6)]*max(placeholder[(cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)])], 0f32)))
-              compute_4[cse_var_55] = (compute_4[cse_var_55] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 7)]*max(placeholder[(cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)])], 0f32)))
-              compute_4[cse_var_44] = (compute_4[cse_var_44] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 8)]*max(placeholder[(cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)])], 0f32)))
-              compute_4[cse_var_43] = (compute_4[cse_var_43] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 9)]*max(placeholder[(cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)])], 0f32)))
-              compute_4[cse_var_99] = (compute_4[cse_var_99] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 10)]*max(placeholder[(cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)])], 0f32)))
-              compute_4[cse_var_88] = (compute_4[cse_var_88] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 11)]*max(placeholder[(cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)])], 0f32)))
-              compute_4[cse_var_85] = (compute_4[cse_var_85] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 12)]*max(placeholder[(cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)])], 0f32)))
-              compute_4[cse_var_82] = (compute_4[cse_var_82] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 13)]*max(placeholder[(cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)])], 0f32)))
-              compute_4[cse_var_71] = (compute_4[cse_var_71] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 14)]*max(placeholder[(cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)])], 0f32)))
-              compute_4[cse_var_130] = (compute_4[cse_var_130] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 15)]*max(placeholder[(cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)])], 0f32)))
-              compute_4[cse_var_8] = (compute_4[cse_var_8] + (placeholder_1[((placeholder_3[cse_var_1]*16) + cse_var_38)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 256)], 0f32)))
-              compute_4[cse_var_9] = (compute_4[cse_var_9] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 1)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 256)], 0f32)))
-              compute_4[cse_var_10] = (compute_4[cse_var_10] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 2)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 256)], 0f32)))
-              compute_4[cse_var_11] = (compute_4[cse_var_11] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 3)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 256)], 0f32)))
-              compute_4[cse_var_3] = (compute_4[cse_var_3] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 4)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 256)], 0f32)))
-              compute_4[cse_var_13] = (compute_4[cse_var_13] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 5)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 256)], 0f32)))
-              compute_4[cse_var_14] = (compute_4[cse_var_14] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 6)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 256)], 0f32)))
-              compute_4[cse_var_15] = (compute_4[cse_var_15] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 7)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 256)], 0f32)))
-              compute_4[cse_var_17] = (compute_4[cse_var_17] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 8)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 256)], 0f32)))
-              compute_4[cse_var_18] = (compute_4[cse_var_18] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 9)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 256)], 0f32)))
-              compute_4[cse_var_19] = (compute_4[cse_var_19] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 10)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 256)], 0f32)))
-              compute_4[cse_var_20] = (compute_4[cse_var_20] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 11)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 256)], 0f32)))
-              compute_4[cse_var_67] = (compute_4[cse_var_67] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 12)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 256)], 0f32)))
-              compute_4[cse_var_66] = (compute_4[cse_var_66] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 13)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 256)], 0f32)))
-              compute_4[cse_var_65] = (compute_4[cse_var_65] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 14)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 256)], 0f32)))
-              compute_4[cse_var_64] = (compute_4[cse_var_64] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 15)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 256)], 0f32)))
-              compute_4[cse_var_61] = (compute_4[cse_var_61] + (placeholder_1[((placeholder_3[cse_var_1]*16) + cse_var_38)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 512)], 0f32)))
-              compute_4[cse_var_60] = (compute_4[cse_var_60] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 1)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 512)], 0f32)))
-              compute_4[cse_var_59] = (compute_4[cse_var_59] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 2)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 512)], 0f32)))
-              compute_4[cse_var_58] = (compute_4[cse_var_58] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 3)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 512)], 0f32)))
-              compute_4[cse_var_57] = (compute_4[cse_var_57] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 4)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 512)], 0f32)))
-              compute_4[cse_var_56] = (compute_4[cse_var_56] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 5)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 512)], 0f32)))
-              compute_4[cse_var_54] = (compute_4[cse_var_54] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 6)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 512)], 0f32)))
-              compute_4[cse_var_53] = (compute_4[cse_var_53] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 7)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 512)], 0f32)))
-              compute_4[cse_var_36] = (compute_4[cse_var_36] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 8)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 512)], 0f32)))
-              compute_4[cse_var_51] = (compute_4[cse_var_51] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 9)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 512)], 0f32)))
-              compute_4[cse_var_50] = (compute_4[cse_var_50] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 10)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 512)], 0f32)))
-              compute_4[cse_var_49] = (compute_4[cse_var_49] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 11)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 512)], 0f32)))
-              compute_4[cse_var_48] = (compute_4[cse_var_48] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 12)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 512)], 0f32)))
-              compute_4[cse_var_47] = (compute_4[cse_var_47] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 13)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 512)], 0f32)))
-              compute_4[cse_var_46] = (compute_4[cse_var_46] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 14)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 512)], 0f32)))
-              compute_4[cse_var_45] = (compute_4[cse_var_45] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 15)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 512)], 0f32)))
-              compute_4[cse_var_42] = (compute_4[cse_var_42] + (placeholder_1[((placeholder_3[cse_var_1]*16) + cse_var_38)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 768)], 0f32)))
-              compute_4[cse_var_41] = (compute_4[cse_var_41] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 1)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 768)], 0f32)))
-              compute_4[cse_var_40] = (compute_4[cse_var_40] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 2)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 768)], 0f32)))
-              compute_4[cse_var_39] = (compute_4[cse_var_39] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 3)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 768)], 0f32)))
-              compute_4[cse_var_98] = (compute_4[cse_var_98] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 4)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 768)], 0f32)))
-              compute_4[cse_var_97] = (compute_4[cse_var_97] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 5)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 768)], 0f32)))
-              compute_4[cse_var_96] = (compute_4[cse_var_96] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 6)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 768)], 0f32)))
-              compute_4[cse_var_95] = (compute_4[cse_var_95] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 7)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 768)], 0f32)))
-              compute_4[cse_var_94] = (compute_4[cse_var_94] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 8)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 768)], 0f32)))
-              compute_4[cse_var_93] = (compute_4[cse_var_93] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 9)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 768)], 0f32)))
-              compute_4[cse_var_92] = (compute_4[cse_var_92] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 10)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 768)], 0f32)))
-              compute_4[cse_var_91] = (compute_4[cse_var_91] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 11)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 768)], 0f32)))
-              compute_4[cse_var_90] = (compute_4[cse_var_90] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 12)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 768)], 0f32)))
-              compute_4[cse_var_89] = (compute_4[cse_var_89] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 13)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 768)], 0f32)))
-              compute_4[cse_var_87] = (compute_4[cse_var_87] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 14)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 768)], 0f32)))
-              compute_4[cse_var_86] = (compute_4[cse_var_86] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 15)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 768)], 0f32)))
-              compute_4[cse_var_68] = (compute_4[cse_var_68] + (placeholder_1[((placeholder_3[cse_var_1]*16) + cse_var_38)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1024)], 0f32)))
-              compute_4[cse_var_83] = (compute_4[cse_var_83] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 1)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1024)], 0f32)))
-              compute_4[cse_var_81] = (compute_4[cse_var_81] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 2)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1024)], 0f32)))
-              compute_4[cse_var_80] = (compute_4[cse_var_80] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 3)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1024)], 0f32)))
-              compute_4[cse_var_79] = (compute_4[cse_var_79] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 4)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1024)], 0f32)))
-              compute_4[cse_var_78] = (compute_4[cse_var_78] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 5)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1024)], 0f32)))
-              compute_4[cse_var_77] = (compute_4[cse_var_77] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 6)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1024)], 0f32)))
-              compute_4[cse_var_76] = (compute_4[cse_var_76] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 7)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1024)], 0f32)))
-              compute_4[cse_var_75] = (compute_4[cse_var_75] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 8)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1024)], 0f32)))
-              compute_4[cse_var_74] = (compute_4[cse_var_74] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 9)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1024)], 0f32)))
-              compute_4[cse_var_73] = (compute_4[cse_var_73] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 10)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1024)], 0f32)))
-              compute_4[cse_var_72] = (compute_4[cse_var_72] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 11)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1024)], 0f32)))
-              compute_4[cse_var_70] = (compute_4[cse_var_70] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 12)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1024)], 0f32)))
-              compute_4[cse_var_69] = (compute_4[cse_var_69] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 13)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1024)], 0f32)))
-              compute_4[cse_var_84] = (compute_4[cse_var_84] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 14)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1024)], 0f32)))
-              compute_4[cse_var_131] = (compute_4[cse_var_131] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 15)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1024)], 0f32)))
-              compute_4[cse_var_129] = (compute_4[cse_var_129] + (placeholder_1[((placeholder_3[cse_var_1]*16) + cse_var_38)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1280)], 0f32)))
-              compute_4[cse_var_128] = (compute_4[cse_var_128] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 1)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1280)], 0f32)))
-              compute_4[cse_var_127] = (compute_4[cse_var_127] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 2)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1280)], 0f32)))
-              compute_4[cse_var_126] = (compute_4[cse_var_126] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 3)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1280)], 0f32)))
-              compute_4[cse_var_125] = (compute_4[cse_var_125] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 4)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1280)], 0f32)))
-              compute_4[cse_var_124] = (compute_4[cse_var_124] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 5)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1280)], 0f32)))
-              compute_4[cse_var_123] = (compute_4[cse_var_123] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 6)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1280)], 0f32)))
-              compute_4[cse_var_122] = (compute_4[cse_var_122] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 7)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1280)], 0f32)))
-              compute_4[cse_var_121] = (compute_4[cse_var_121] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 8)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1280)], 0f32)))
-              compute_4[cse_var_120] = (compute_4[cse_var_120] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 9)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1280)], 0f32)))
-              compute_4[cse_var_119] = (compute_4[cse_var_119] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 10)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1280)], 0f32)))
-              compute_4[cse_var_118] = (compute_4[cse_var_118] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 11)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1280)], 0f32)))
-              compute_4[cse_var_117] = (compute_4[cse_var_117] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 12)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1280)], 0f32)))
-              compute_4[cse_var_100] = (compute_4[cse_var_100] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 13)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1280)], 0f32)))
-              compute_4[cse_var_115] = (compute_4[cse_var_115] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 14)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1280)], 0f32)))
-              compute_4[cse_var_114] = (compute_4[cse_var_114] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 15)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1280)], 0f32)))
-              compute_4[cse_var_113] = (compute_4[cse_var_113] + (placeholder_1[((placeholder_3[cse_var_1]*16) + cse_var_38)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1536)], 0f32)))
-              compute_4[cse_var_112] = (compute_4[cse_var_112] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 1)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1536)], 0f32)))
-              compute_4[cse_var_111] = (compute_4[cse_var_111] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 2)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1536)], 0f32)))
-              compute_4[cse_var_110] = (compute_4[cse_var_110] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 3)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1536)], 0f32)))
-              compute_4[cse_var_109] = (compute_4[cse_var_109] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 4)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1536)], 0f32)))
-              compute_4[cse_var_108] = (compute_4[cse_var_108] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 5)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1536)], 0f32)))
-              compute_4[cse_var_107] = (compute_4[cse_var_107] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 6)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1536)], 0f32)))
-              compute_4[cse_var_106] = (compute_4[cse_var_106] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 7)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1536)], 0f32)))
-              compute_4[cse_var_104] = (compute_4[cse_var_104] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 8)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1536)], 0f32)))
-              compute_4[cse_var_103] = (compute_4[cse_var_103] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 9)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1536)], 0f32)))
-              compute_4[cse_var_102] = (compute_4[cse_var_102] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 10)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1536)], 0f32)))
-              compute_4[cse_var_101] = (compute_4[cse_var_101] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 11)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1536)], 0f32)))
-              compute_4[cse_var_52] = (compute_4[cse_var_52] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 12)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1536)], 0f32)))
-              compute_4[cse_var_35] = (compute_4[cse_var_35] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 13)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1536)], 0f32)))
-              compute_4[cse_var_34] = (compute_4[cse_var_34] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 14)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1536)], 0f32)))
-              compute_4[cse_var_33] = (compute_4[cse_var_33] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 15)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1536)], 0f32)))
-              compute_4[cse_var_32] = (compute_4[cse_var_32] + (placeholder_1[((placeholder_3[cse_var_1]*16) + cse_var_38)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1792)], 0f32)))
-              compute_4[cse_var_31] = (compute_4[cse_var_31] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 1)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1792)], 0f32)))
-              compute_4[cse_var_30] = (compute_4[cse_var_30] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 2)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1792)], 0f32)))
-              compute_4[cse_var_29] = (compute_4[cse_var_29] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 3)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1792)], 0f32)))
-              compute_4[cse_var_28] = (compute_4[cse_var_28] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 4)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1792)], 0f32)))
-              compute_4[cse_var_27] = (compute_4[cse_var_27] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 5)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1792)], 0f32)))
-              compute_4[cse_var_26] = (compute_4[cse_var_26] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 6)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1792)], 0f32)))
-              compute_4[cse_var_25] = (compute_4[cse_var_25] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 7)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1792)], 0f32)))
-              compute_4[cse_var_24] = (compute_4[cse_var_24] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 8)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1792)], 0f32)))
-              compute_4[cse_var_23] = (compute_4[cse_var_23] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 9)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1792)], 0f32)))
-              compute_4[cse_var_22] = (compute_4[cse_var_22] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 10)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1792)], 0f32)))
-              compute_4[cse_var_21] = (compute_4[cse_var_21] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 11)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1792)], 0f32)))
-              compute_4[cse_var_12] = (compute_4[cse_var_12] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 12)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1792)], 0f32)))
-              compute_4[cse_var_4] = (compute_4[cse_var_4] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 13)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1792)], 0f32)))
-              compute_4[cse_var_5] = (compute_4[cse_var_5] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 14)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1792)], 0f32)))
-              compute_4[cse_var_6] = (compute_4[cse_var_6] + (placeholder_1[(((placeholder_3[cse_var_1]*16) + cse_var_38) + 15)]*max(placeholder[((cse_var_37 + placeholder_2[(placeholder_3[cse_var_1] + elem_idx)]) + 1792)], 0f32)))
+              compute_4: Buffer(compute_3, float32, [2048], [])[cse_var_1] = 0f32
+              compute_4[(cse_var_1 + 1)] = 0f32
+              compute_4[(cse_var_1 + 2)] = 0f32
+              compute_4[(cse_var_1 + 3)] = 0f32
+              compute_4[(cse_var_1 + 4)] = 0f32
+              compute_4[(cse_var_1 + 5)] = 0f32
+              compute_4[(cse_var_1 + 6)] = 0f32
+              compute_4[(cse_var_1 + 7)] = 0f32
+              compute_4[(cse_var_1 + 8)] = 0f32
+              compute_4[(cse_var_1 + 9)] = 0f32
+              compute_4[(cse_var_1 + 10)] = 0f32
+              compute_4[(cse_var_1 + 11)] = 0f32
+              compute_4[(cse_var_1 + 12)] = 0f32
+              compute_4[(cse_var_1 + 13)] = 0f32
+              compute_4[(cse_var_1 + 14)] = 0f32
+              compute_4[(cse_var_1 + 15)] = 0f32
+            }
+          }
+          for (elem_idx: int32, 0, let cse_var_2: int32 = ((floormod(i0.outer.i1.outer.fused, 16)*2) + nb_j.inner) in (placeholder_3[(cse_var_2 + 1)] - placeholder_3[cse_var_2])) {
+            for (i.inner: int32, 0, 16) {
+              let cse_var_21: int32 = (elem_idx*16)
+              let cse_var_20: int32 = ((floormod(i0.outer.i1.outer.fused, 16)*2) + nb_j.inner)
+              let cse_var_19: int32 = (((i.outer.inner*512) + (i.inner*32)) + (nb_j.inner*16))
+              let cse_var_18: int32 = (cse_var_19 + 1)
+              let cse_var_17: int32 = (cse_var_19 + 11)
+              let cse_var_16: int32 = (cse_var_19 + 12)
+              let cse_var_15: int32 = (cse_var_19 + 13)
+              let cse_var_14: int32 = (cse_var_19 + 14)
+              let cse_var_13: int32 = (cse_var_19 + 15)
+              let cse_var_12: int32 = (cse_var_19 + 2)
+              let cse_var_11: int32 = (cse_var_19 + 3)
+              let cse_var_10: int32 = (cse_var_19 + 4)
+              let cse_var_9: int32 = (cse_var_19 + 5)
+              let cse_var_8: int32 = (cse_var_19 + 6)
+              let cse_var_7: int32 = (cse_var_19 + 7)
+              let cse_var_6: int32 = (cse_var_19 + 8)
+              let cse_var_5: int32 = (cse_var_19 + 9)
+              let cse_var_4: int32 = (((floordiv(i0.outer.i1.outer.fused, 16)*16384) + (i.outer.inner*4096)) + (i.inner*256))
+              let cse_var_3: int32 = (cse_var_19 + 10)
+               {
+                compute_4[cse_var_19] = (compute_4[cse_var_19] + (placeholder_1[((placeholder_3[cse_var_20]*16) + cse_var_21)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+                compute_4[cse_var_18] = (compute_4[cse_var_18] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 1)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+                compute_4[cse_var_12] = (compute_4[cse_var_12] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 2)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+                compute_4[cse_var_11] = (compute_4[cse_var_11] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 3)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+                compute_4[cse_var_10] = (compute_4[cse_var_10] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 4)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+                compute_4[cse_var_9] = (compute_4[cse_var_9] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 5)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+                compute_4[cse_var_8] = (compute_4[cse_var_8] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 6)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+                compute_4[cse_var_7] = (compute_4[cse_var_7] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 7)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+                compute_4[cse_var_6] = (compute_4[cse_var_6] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 8)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+                compute_4[cse_var_5] = (compute_4[cse_var_5] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 9)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+                compute_4[cse_var_3] = (compute_4[cse_var_3] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 10)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+                compute_4[cse_var_17] = (compute_4[cse_var_17] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 11)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+                compute_4[cse_var_16] = (compute_4[cse_var_16] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 12)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+                compute_4[cse_var_15] = (compute_4[cse_var_15] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 13)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+                compute_4[cse_var_14] = (compute_4[cse_var_14] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 14)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+                compute_4[cse_var_13] = (compute_4[cse_var_13] + (placeholder_1[(((placeholder_3[cse_var_20]*16) + cse_var_21) + 15)]*max(placeholder[(cse_var_4 + placeholder_2[(placeholder_3[cse_var_20] + elem_idx)])], 0f32)))
+              }
             }
           }
         }
       }
-      for (i0.inner: int32, 0, 8) {
-        let cse_var_132: int32 = (((i0.outer*4096) + (i0.inner*512)) + (i1.outer*32))
-        compute[ramp(cse_var_132, 1, 32)] = max((compute_4[ramp((i0.inner*32), 1, 32)] + placeholder_4[ramp(cse_var_132, 1, 32)]), broadcast(0f32, 32))
+      for (i0.inner: int32, 0, 64) {
+        let cse_var_22: int32 = (((floordiv(i0.outer.i1.outer.fused, 16)*32768) + (i0.inner*512)) + (floormod(i0.outer.i1.outer.fused, 16)*32))
+        compute[ramp(cse_var_22, 1, 32)] = max((compute_4[ramp((i0.inner*32), 1, 32)] + placeholder_4[ramp(cse_var_22, 1, 32)]), broadcast(0f32, 32))
       }
     }
   }
@@ -1039,7 +709,7 @@ layout transformation, parallelization, vectorization, unrolling, and operator f
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time of this operator: 2.706 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time of this operator: 1.721 ms
 </pre></div>
 </div>
 <div class="admonition note">
diff --git a/docs/how_to/tune_with_autotvm/sg_execution_times.html b/docs/how_to/tune_with_autotvm/sg_execution_times.html
index 135de22e1..83215748f 100644
--- a/docs/how_to/tune_with_autotvm/sg_execution_times.html
+++ b/docs/how_to/tune_with_autotvm/sg_execution_times.html
@@ -300,13 +300,13 @@
             
   <div class="section" id="computation-times">
 <span id="sphx-glr-how-to-tune-with-autotvm-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>00:43.955</strong> total execution time for <strong>how_to_tune_with_autotvm</strong> files:</p>
+<p><strong>00:44.244</strong> total execution time for <strong>how_to_tune_with_autotvm</strong> files:</p>
 <ul class="simple">
-<li><p><strong>00:43.085</strong>: <a class="reference internal" href="tune_conv2d_cuda.html#sphx-glr-how-to-tune-with-autotvm-tune-conv2d-cuda-py"><span class="std std-ref">Tuning High Performance Convolution on NVIDIA GPUs</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_conv2d_cuda.py</span></code>)</p></li>
-<li><p><strong>00:00.228</strong>: <a class="reference internal" href="tune_relay_x86.html#sphx-glr-how-to-tune-with-autotvm-tune-relay-x86-py"><span class="std std-ref">Auto-tuning a Convolutional Network for x86 CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_relay_x86.py</span></code>)</p></li>
-<li><p><strong>00:00.216</strong>: <a class="reference internal" href="tune_relay_arm.html#sphx-glr-how-to-tune-with-autotvm-tune-relay-arm-py"><span class="std std-ref">Auto-tuning a Convolutional Network for ARM CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_relay_arm.py</span></code>)</p></li>
-<li><p><strong>00:00.213</strong>: <a class="reference internal" href="tune_relay_mobile_gpu.html#sphx-glr-how-to-tune-with-autotvm-tune-relay-mobile-gpu-py"><span class="std std-ref">Auto-tuning a Convolutional Network for Mobile GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_relay_mobile_gpu.py</span></code>)</p></li>
-<li><p><strong>00:00.213</strong>: <a class="reference internal" href="tune_relay_cuda.html#sphx-glr-how-to-tune-with-autotvm-tune-relay-cuda-py"><span class="std std-ref">Auto-tuning a Convolutional Network for NVIDIA GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_relay_cuda.py</span></code>)</p></li>
+<li><p><strong>00:43.367</strong>: <a class="reference internal" href="tune_conv2d_cuda.html#sphx-glr-how-to-tune-with-autotvm-tune-conv2d-cuda-py"><span class="std std-ref">Tuning High Performance Convolution on NVIDIA GPUs</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_conv2d_cuda.py</span></code>)</p></li>
+<li><p><strong>00:00.232</strong>: <a class="reference internal" href="tune_relay_x86.html#sphx-glr-how-to-tune-with-autotvm-tune-relay-x86-py"><span class="std std-ref">Auto-tuning a Convolutional Network for x86 CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_relay_x86.py</span></code>)</p></li>
+<li><p><strong>00:00.218</strong>: <a class="reference internal" href="tune_relay_cuda.html#sphx-glr-how-to-tune-with-autotvm-tune-relay-cuda-py"><span class="std std-ref">Auto-tuning a Convolutional Network for NVIDIA GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_relay_cuda.py</span></code>)</p></li>
+<li><p><strong>00:00.214</strong>: <a class="reference internal" href="tune_relay_mobile_gpu.html#sphx-glr-how-to-tune-with-autotvm-tune-relay-mobile-gpu-py"><span class="std std-ref">Auto-tuning a Convolutional Network for Mobile GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_relay_mobile_gpu.py</span></code>)</p></li>
+<li><p><strong>00:00.214</strong>: <a class="reference internal" href="tune_relay_arm.html#sphx-glr-how-to-tune-with-autotvm-tune-relay-arm-py"><span class="std std-ref">Auto-tuning a Convolutional Network for ARM CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_relay_arm.py</span></code>)</p></li>
 </ul>
 </div>
 
diff --git a/docs/how_to/tune_with_autotvm/tune_conv2d_cuda.html b/docs/how_to/tune_with_autotvm/tune_conv2d_cuda.html
index 560080f20..32f662078 100644
--- a/docs/how_to/tune_with_autotvm/tune_conv2d_cuda.html
+++ b/docs/how_to/tune_with_autotvm/tune_conv2d_cuda.html
@@ -1142,8 +1142,8 @@ Traceback (most recent call last):
   File &quot;/workspace/python/tvm/autotvm/measure/measure_methods.py&quot;, line 854, in verify_pass
     raise InstantiationError(&quot;Skipped because of invalid gpu kernel&quot;)
 tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel        [(&#39;tile_f&#39;, [-1, 4, 4, 32]), (&#39;tile_y&#39;, [-1, 1, 1, 7]), (&#39;tile_x&#39;, [-1, 1, 7, 1]), (&#39;tile_rc&#39;, [-1, 1, 128]), (&#39;tile_ry&#39;, [-1, 1, 3]), (&#39;tile_rx&#39;, [-1, 3, 1]), (&#39;auto_unroll_max_step&#39;, 512), (&#39;unroll_explicit&#39;, 0)],None,2885496
-No: 6   GFLOPS: 103.76/103.76   result: MeasureResult(costs=(0.0022312065416666667,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.5933806896209717, timestamp=1650065132.1649663)      [(&#39;tile_f&#39;, [-1, 1, 1, 1]), (&#39;tile_y&#39;, [-1, 1, 1, 1]), (&#39;tile_x&#39;, [-1, 1, 7, 1]), (&#39;tile_rc&#39;, [-1, 4, 4]), (&#39;tile_ry&#39;, [-1, 3, 1]), (&#39;tile_rx&#39;, [-1, 1, 1]), (&#39;auto_unroll_max_step&#39;, 1500), (&#39;unroll_explicit&#39;, 0)],None,3754080
-No: 7   GFLOPS: 0.00/103.76     result: Traceback (most recent call last):
+No: 6   GFLOPS: 42.31/42.31     result: MeasureResult(costs=(0.005471151894736842,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.6274833679199219, timestamp=1650168007.1835592)       [(&#39;tile_f&#39;, [-1, 1, 1, 1]), (&#39;tile_y&#39;, [-1, 1, 1, 1]), (&#39;tile_x&#39;, [-1, 1, 7, 1]), (&#39;tile_rc&#39;, [-1, 4, 4]), (&#39;tile_ry&#39;, [-1, 3, 1]), (&#39;tile_rx&#39;, [-1, 1, 1]), (&#39;auto_unroll_max_step&#39;, 1500), (&#39;unroll_explicit&#39;, 0)],None,3754080
+No: 7   GFLOPS: 0.00/42.31      result: Traceback (most recent call last):
   File &quot;/workspace/python/tvm/autotvm/measure/measure_methods.py&quot;, line 571, in __call__
     func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
   File &quot;/workspace/python/tvm/autotvm/measure/measure_methods.py&quot;, line 523, in _build_func_common
@@ -1266,7 +1266,7 @@ Traceback (most recent call last):
   File &quot;/workspace/python/tvm/autotvm/measure/measure_methods.py&quot;, line 854, in verify_pass
     raise InstantiationError(&quot;Skipped because of invalid gpu kernel&quot;)
 tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel        [(&#39;tile_f&#39;, [-1, 1, 16, 32]), (&#39;tile_y&#39;, [-1, 1, 1, 1]), (&#39;tile_x&#39;, [-1, 1, 7, 1]), (&#39;tile_rc&#39;, [-1, 256, 1]), (&#39;tile_ry&#39;, [-1, 1, 3]), (&#39;tile_rx&#39;, [-1, 3, 1]), (&#39;auto_unroll_max_step&#39;, 0), (&#39;unroll_explicit&#39;, 1)],None,6225319
-No: 8   GFLOPS: 0.00/103.76     result: Traceback (most recent call last):
+No: 8   GFLOPS: 0.00/42.31      result: Traceback (most recent call last):
   File &quot;/workspace/python/tvm/autotvm/measure/measure_methods.py&quot;, line 571, in __call__
     func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
   File &quot;/workspace/python/tvm/autotvm/measure/measure_methods.py&quot;, line 523, in _build_func_common
@@ -1389,7 +1389,7 @@ Traceback (most recent call last):
   File &quot;/workspace/python/tvm/autotvm/measure/measure_methods.py&quot;, line 854, in verify_pass
     raise InstantiationError(&quot;Skipped because of invalid gpu kernel&quot;)
 tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel        [(&#39;tile_f&#39;, [-1, 2, 1, 32]), (&#39;tile_y&#39;, [-1, 1, 1, 1]), (&#39;tile_x&#39;, [-1, 1, 1, 1]), (&#39;tile_rc&#39;, [-1, 8, 64]), (&#39;tile_ry&#39;, [-1, 3, 1]), (&#39;tile_rx&#39;, [-1, 3, 1]), (&#39;auto_unroll_max_step&#39;, 0), (&#39;unroll_explicit&#39;, 0)],None,943546
-No: 9   GFLOPS: 0.00/103.76     result: Traceback (most recent call last):
+No: 9   GFLOPS: 0.00/42.31      result: Traceback (most recent call last):
   File &quot;/workspace/python/tvm/autotvm/measure/measure_methods.py&quot;, line 571, in __call__
     func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
   File &quot;/workspace/python/tvm/autotvm/measure/measure_methods.py&quot;, line 523, in _build_func_common
@@ -1512,7 +1512,7 @@ Traceback (most recent call last):
   File &quot;/workspace/python/tvm/autotvm/measure/measure_methods.py&quot;, line 854, in verify_pass
     raise InstantiationError(&quot;Skipped because of invalid gpu kernel&quot;)
 tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel        [(&#39;tile_f&#39;, [-1, 4, 16, 4]), (&#39;tile_y&#39;, [-1, 1, 1, 7]), (&#39;tile_x&#39;, [-1, 1, 1, 7]), (&#39;tile_rc&#39;, [-1, 16, 32]), (&#39;tile_ry&#39;, [-1, 1, 3]), (&#39;tile_rx&#39;, [-1, 3, 1]), (&#39;auto_unroll_max_step&#39;, 512), (&#39;unroll_explicit&#39;, 0)],None,2868708
-No: 10  GFLOPS: 0.00/103.76     result: Traceback (most recent call last):
+No: 10  GFLOPS: 0.00/42.31      result: Traceback (most recent call last):
   File &quot;/workspace/python/tvm/autotvm/measure/measure_methods.py&quot;, line 142, in build
     res = future.result()
   File &quot;/usr/lib/python3.7/concurrent/futures/_base.py&quot;, line 435, in result
@@ -1530,7 +1530,7 @@ No: 10  GFLOPS: 0.00/103.76     result: Traceback (most recent call last):
 TimeoutError
 
         [(&#39;tile_f&#39;, [-1, 32, 2, 4]), (&#39;tile_y&#39;, [-1, 1, 7, 1]), (&#39;tile_x&#39;, [-1, 1, 1, 7]), (&#39;tile_rc&#39;, [-1, 4, 2]), (&#39;tile_ry&#39;, [-1, 1, 1]), (&#39;tile_rx&#39;, [-1, 1, 3]), (&#39;auto_unroll_max_step&#39;, 1500), (&#39;unroll_explicit&#39;, 0)],None,4691833
-No: 11  GFLOPS: 0.00/103.76     result: Traceback (most recent call last):
+No: 11  GFLOPS: 0.00/42.31      result: Traceback (most recent call last):
   File &quot;/workspace/python/tvm/autotvm/measure/measure_methods.py&quot;, line 571, in __call__
     func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
   File &quot;/workspace/python/tvm/autotvm/measure/measure_methods.py&quot;, line 523, in _build_func_common
@@ -1653,7 +1653,7 @@ Traceback (most recent call last):
   File &quot;/workspace/python/tvm/autotvm/measure/measure_methods.py&quot;, line 854, in verify_pass
     raise InstantiationError(&quot;Skipped because of invalid gpu kernel&quot;)
 tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel        [(&#39;tile_f&#39;, [-1, 1, 2, 64]), (&#39;tile_y&#39;, [-1, 1, 1, 1]), (&#39;tile_x&#39;, [-1, 1, 1, 1]), (&#39;tile_rc&#39;, [-1, 4, 4]), (&#39;tile_ry&#39;, [-1, 1, 3]), (&#39;tile_rx&#39;, [-1, 3, 1]), (&#39;auto_unroll_max_step&#39;, 0), (&#39;unroll_explicit&#39;, 0)],None,1042124
-No: 12  GFLOPS: 0.00/103.76     result: Traceback (most recent call last):
+No: 12  GFLOPS: 0.00/42.31      result: Traceback (most recent call last):
   File &quot;/workspace/python/tvm/autotvm/measure/measure_methods.py&quot;, line 571, in __call__
     func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
   File &quot;/workspace/python/tvm/autotvm/measure/measure_methods.py&quot;, line 523, in _build_func_common
@@ -1776,7 +1776,7 @@ Traceback (most recent call last):
   File &quot;/workspace/python/tvm/autotvm/measure/measure_methods.py&quot;, line 854, in verify_pass
     raise InstantiationError(&quot;Skipped because of invalid gpu kernel&quot;)
 tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel        [(&#39;tile_f&#39;, [-1, 32, 1, 4]), (&#39;tile_y&#39;, [-1, 1, 1, 7]), (&#39;tile_x&#39;, [-1, 1, 7, 1]), (&#39;tile_rc&#39;, [-1, 32, 16]), (&#39;tile_ry&#39;, [-1, 1, 1]), (&#39;tile_rx&#39;, [-1, 1, 3]), (&#39;auto_unroll_max_step&#39;, 1500), (&#39;unroll_explicit&#39;, 1)],None,10013405
-No: 13  GFLOPS: 0.00/103.76     result: Traceback (most recent call last):
+No: 13  GFLOPS: 0.00/42.31      result: Traceback (most recent call last):
   File &quot;/workspace/python/tvm/autotvm/measure/measure_methods.py&quot;, line 571, in __call__
     func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
   File &quot;/workspace/python/tvm/autotvm/measure/measure_methods.py&quot;, line 523, in _build_func_common
@@ -1899,7 +1899,7 @@ Traceback (most recent call last):
   File &quot;/workspace/python/tvm/autotvm/measure/measure_methods.py&quot;, line 854, in verify_pass
     raise InstantiationError(&quot;Skipped because of invalid gpu kernel&quot;)
 tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel        [(&#39;tile_f&#39;, [-1, 8, 8, 2]), (&#39;tile_y&#39;, [-1, 1, 1, 1]), (&#39;tile_x&#39;, [-1, 1, 7, 1]), (&#39;tile_rc&#39;, [-1, 4, 32]), (&#39;tile_ry&#39;, [-1, 3, 1]), (&#39;tile_rx&#39;, [-1, 1, 3]), (&#39;auto_unroll_max_step&#39;, 0), (&#39;unroll_explicit&#39;, 1)],None,6732082
-No: 14  GFLOPS: 0.00/103.76     result: Traceback (most recent call last):
+No: 14  GFLOPS: 0.00/42.31      result: Traceback (most recent call last):
   File &quot;/workspace/python/tvm/autotvm/measure/measure_methods.py&quot;, line 571, in __call__
     func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
   File &quot;/workspace/python/tvm/autotvm/measure/measure_methods.py&quot;, line 523, in _build_func_common
@@ -2022,7 +2022,7 @@ Traceback (most recent call last):
   File &quot;/workspace/python/tvm/autotvm/measure/measure_methods.py&quot;, line 854, in verify_pass
     raise InstantiationError(&quot;Skipped because of invalid gpu kernel&quot;)
 tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel        [(&#39;tile_f&#39;, [-1, 2, 4, 32]), (&#39;tile_y&#39;, [-1, 7, 1, 1]), (&#39;tile_x&#39;, [-1, 1, 1, 1]), (&#39;tile_rc&#39;, [-1, 4, 128]), (&#39;tile_ry&#39;, [-1, 1, 3]), (&#39;tile_rx&#39;, [-1, 1, 1]), (&#39;auto_unroll_max_step&#39;, 512), (&#39;unroll_explicit&#39;, 1)],None,7536735
-No: 15  GFLOPS: 0.00/103.76     result: Traceback (most recent call last):
+No: 15  GFLOPS: 0.00/42.31      result: Traceback (most recent call last):
   File &quot;/workspace/python/tvm/autotvm/measure/measure_methods.py&quot;, line 571, in __call__
     func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
   File &quot;/workspace/python/tvm/autotvm/measure/measure_methods.py&quot;, line 523, in _build_func_common
@@ -2145,7 +2145,7 @@ Traceback (most recent call last):
   File &quot;/workspace/python/tvm/autotvm/measure/measure_methods.py&quot;, line 854, in verify_pass
     raise InstantiationError(&quot;Skipped because of invalid gpu kernel&quot;)
 tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel        [(&#39;tile_f&#39;, [-1, 2, 1, 4]), (&#39;tile_y&#39;, [-1, 1, 1, 7]), (&#39;tile_x&#39;, [-1, 1, 1, 7]), (&#39;tile_rc&#39;, [-1, 128, 4]), (&#39;tile_ry&#39;, [-1, 1, 3]), (&#39;tile_rx&#39;, [-1, 1, 1]), (&#39;auto_unroll_max_step&#39;, 0), (&#39;unroll_explicit&#39;, 0)],None,482121
-No: 16  GFLOPS: 0.00/103.76     result: Traceback (most recent call last):
+No: 16  GFLOPS: 0.00/42.31      result: Traceback (most recent call last):
   File &quot;/workspace/python/tvm/autotvm/measure/measure_methods.py&quot;, line 571, in __call__
     func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
   File &quot;/workspace/python/tvm/autotvm/measure/measure_methods.py&quot;, line 523, in _build_func_common
@@ -2268,7 +2268,7 @@ Traceback (most recent call last):
   File &quot;/workspace/python/tvm/autotvm/measure/measure_methods.py&quot;, line 854, in verify_pass
     raise InstantiationError(&quot;Skipped because of invalid gpu kernel&quot;)
 tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel        [(&#39;tile_f&#39;, [-1, 2, 1, 16]), (&#39;tile_y&#39;, [-1, 1, 7, 1]), (&#39;tile_x&#39;, [-1, 7, 1, 1]), (&#39;tile_rc&#39;, [-1, 32, 8]), (&#39;tile_ry&#39;, [-1, 1, 3]), (&#39;tile_rx&#39;, [-1, 3, 1]), (&#39;auto_unroll_max_step&#39;, 512), (&#39;unroll_explicit&#39;, 0)],None,2824525
-No: 17  GFLOPS: 0.00/103.76     result: Traceback (most recent call last):
+No: 17  GFLOPS: 0.00/42.31      result: Traceback (most recent call last):
   File &quot;/workspace/python/tvm/autotvm/measure/measure_methods.py&quot;, line 571, in __call__
     func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
   File &quot;/workspace/python/tvm/autotvm/measure/measure_methods.py&quot;, line 523, in _build_func_common
@@ -2391,7 +2391,7 @@ Traceback (most recent call last):
   File &quot;/workspace/python/tvm/autotvm/measure/measure_methods.py&quot;, line 854, in verify_pass
     raise InstantiationError(&quot;Skipped because of invalid gpu kernel&quot;)
 tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel        [(&#39;tile_f&#39;, [-1, 64, 1, 1]), (&#39;tile_y&#39;, [-1, 1, 1, 1]), (&#39;tile_x&#39;, [-1, 7, 1, 1]), (&#39;tile_rc&#39;, [-1, 8, 8]), (&#39;tile_ry&#39;, [-1, 1, 3]), (&#39;tile_rx&#39;, [-1, 3, 1]), (&#39;auto_unroll_max_step&#39;, 1500), (&#39;unroll_explicit&#39;, 0)],None,4559286
-No: 18  GFLOPS: 0.00/103.76     result: Traceback (most recent call last):
+No: 18  GFLOPS: 0.00/42.31      result: Traceback (most recent call last):
   File &quot;/workspace/python/tvm/autotvm/measure/measure_methods.py&quot;, line 571, in __call__
     func, arg_info = _build_func_common(measure_input, self.runtime, **kwargs)
   File &quot;/workspace/python/tvm/autotvm/measure/measure_methods.py&quot;, line 523, in _build_func_common
@@ -2514,7 +2514,7 @@ Traceback (most recent call last):
   File &quot;/workspace/python/tvm/autotvm/measure/measure_methods.py&quot;, line 854, in verify_pass
     raise InstantiationError(&quot;Skipped because of invalid gpu kernel&quot;)
 tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel        [(&#39;tile_f&#39;, [-1, 1, 32, 16]), (&#39;tile_y&#39;, [-1, 1, 1, 1]), (&#39;tile_x&#39;, [-1, 7, 1, 1]), (&#39;tile_rc&#39;, [-1, 1, 512]), (&#39;tile_ry&#39;, [-1, 3, 1]), (&#39;tile_rx&#39;, [-1, 3, 1]), (&#39;auto_unroll_max_step&#39;, 1500), (&#39;unroll_explicit&#39;, 1)],None,9677544
-No: 19  GFLOPS: 0.00/103.76     result: Traceback (most recent call last):
+No: 19  GFLOPS: 0.00/42.31      result: Traceback (most recent call last):
   File &quot;/workspace/python/tvm/autotvm/measure/measure_methods.py&quot;, line 721, in __call__
     yield remote, remote.load_module(os.path.split(build_result.filename)[1])
   File &quot;/workspace/python/tvm/autotvm/measure/measure_methods.py&quot;, line 685, in run_through_rpc
@@ -2602,7 +2602,7 @@ tvm._ffi.base.TVMError: Traceback (most recent call last):
   15: _PyEval_EvalFrameDefault
   14: 0x0000000000537c30
   13: _PyObject_FastCallKeywords
-  12: 0x00007f0bfab37fa2
+  12: 0x00007f5b7c486fa2
   11: _ctypes_callproc
   10: ffi_call
   9: ffi_call_unix64
@@ -2667,7 +2667,7 @@ Traceback (most recent call last):
   21: _PyFunction_FastCallKeywords
   20: _PyEval_EvalFrameDefault
   19: _PyFunction_FastCall      [(&#39;tile_f&#39;, [-1, 8, 2, 16]), (&#39;tile_y&#39;, [-1, 7, 1, 1]), (&#39;tile_x&#39;, [-1, 7, 1, 1]), (&#39;tile_rc&#39;, [-1, 1, 1]), (&#39;tile_ry&#39;, [-1, 1, 1]), (&#39;tile_rx&#39;, [-1, 1, 3]), (&#39;auto_unroll_max_step&#39;, 0), (&#39;unroll_explicit&#39;, 1)],None,6390073
-No: 20  GFLOPS: 144.55/144.55   result: MeasureResult(costs=(0.0016015775600000002,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.4185152053833008, timestamp=1650065158.492134)       [(&#39;tile_f&#39;, [-1, 1, 4, 1]), (&#39;tile_y&#39;, [-1, 1, 1, 1]), (&#39;tile_x&#39;, [-1, 7, 1, 1]), (&#39;tile_rc&#39;, [-1, 4, 1]), (&#39;tile_ry&#39;, [-1, 1, 1]), (&#39;tile_rx&#39;, [-1, 1, 3]), (&#39;auto_unroll_max_step&#39;, 1500), (&#39;unroll_explicit&#39;, 1)],None,9881539
+No: 20  GFLOPS: 143.76/143.76   result: MeasureResult(costs=(0.0016103786199999997,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.4413950443267822, timestamp=1650168033.6066215)      [(&#39;tile_f&#39;, [-1, 1, 4, 1]), (&#39;tile_y&#39;, [-1, 1, 1, 1]), (&#39;tile_x&#39;, [-1, 7, 1, 1]), (&#39;tile_rc&#39;, [-1, 4, 1]), (&#39;tile_ry&#39;, [-1, 1, 1]), (&#39;tile_rx&#39;, [-1, 1, 3]), (&#39;auto_unroll_max_step&#39;, 1500), (&#39;unroll_explicit&#39;, 1)],None,9881539
 </pre></div>
 </div>
 <p>Finally we can inspect the best config from log file, check correctness,
@@ -2706,7 +2706,7 @@ and measure running time.</p>
 <p class="sphx-glr-script-out">Out:</p>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Best config:
 [(&#39;tile_f&#39;, [-1, 1, 4, 1]), (&#39;tile_y&#39;, [-1, 1, 1, 1]), (&#39;tile_x&#39;, [-1, 7, 1, 1]), (&#39;tile_rc&#39;, [-1, 4, 1]), (&#39;tile_ry&#39;, [-1, 1, 1]), (&#39;tile_rx&#39;, [-1, 1, 3]), (&#39;auto_unroll_max_step&#39;, 1500), (&#39;unroll_explicit&#39;, 1)],None,9881539
-Time cost of this operator: 0.002011
+Time cost of this operator: 0.002038
 </pre></div>
 </div>
 <div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-tune-with-autotvm-tune-conv2d-cuda-py">
diff --git a/docs/how_to/work_with_microtvm/micro_autotune.html b/docs/how_to/work_with_microtvm/micro_autotune.html
index 8b853d9da..3fa8c7d83 100644
--- a/docs/how_to/work_with_microtvm/micro_autotune.html
+++ b/docs/how_to/work_with_microtvm/micro_autotune.html
@@ -553,10 +553,10 @@ the tuned operator.</p>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>########## Build without Autotuning ##########
 Node Name                                     Ops                                           Time(us)  Time(%)  Shape              Inputs  Outputs
 ---------                                     ---                                           --------  -------  -----              ------  -------
-tvmgen_default_fused_nn_contrib_conv2d_NCHWc  tvmgen_default_fused_nn_contrib_conv2d_NCHWc  313.0     98.744   (1, 2, 10, 10, 3)  2       1
-tvmgen_default_fused_layout_transform_1       tvmgen_default_fused_layout_transform_1       3.073     0.969    (1, 6, 10, 10)     1       1
-tvmgen_default_fused_layout_transform         tvmgen_default_fused_layout_transform         0.907     0.286    (1, 1, 10, 10, 3)  1       1
-Total_time                                    -                                             316.98    -        -                  -       -
+tvmgen_default_fused_nn_contrib_conv2d_NCHWc  tvmgen_default_fused_nn_contrib_conv2d_NCHWc  313.4     98.695   (1, 2, 10, 10, 3)  2       1
+tvmgen_default_fused_layout_transform_1       tvmgen_default_fused_layout_transform_1       3.153     0.993    (1, 6, 10, 10)     1       1
+tvmgen_default_fused_layout_transform         tvmgen_default_fused_layout_transform         0.991     0.312    (1, 1, 10, 10, 3)  1       1
+Total_time                                    -                                             317.544   -        -                  -       -
 </pre></div>
 </div>
 </div>
@@ -608,10 +608,10 @@ Total_time                                    -
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>########## Build with Autotuning ##########
 Node Name                                     Ops                                           Time(us)  Time(%)  Shape              Inputs  Outputs
 ---------                                     ---                                           --------  -------  -----              ------  -------
-tvmgen_default_fused_nn_contrib_conv2d_NCHWc  tvmgen_default_fused_nn_contrib_conv2d_NCHWc  133.7     98.082   (1, 6, 10, 10, 1)  2       1
-tvmgen_default_fused_layout_transform_1       tvmgen_default_fused_layout_transform_1       1.702     1.248    (1, 6, 10, 10)     1       1
-tvmgen_default_fused_layout_transform         tvmgen_default_fused_layout_transform         0.913     0.67     (1, 1, 10, 10, 3)  1       1
-Total_time                                    -                                             136.314   -        -                  -       -
+tvmgen_default_fused_nn_contrib_conv2d_NCHWc  tvmgen_default_fused_nn_contrib_conv2d_NCHWc  324.8     98.792   (1, 2, 10, 10, 3)  2       1
+tvmgen_default_fused_layout_transform_1       tvmgen_default_fused_layout_transform_1       3.07      0.934    (1, 6, 10, 10)     1       1
+tvmgen_default_fused_layout_transform         tvmgen_default_fused_layout_transform         0.901     0.274    (1, 1, 10, 10, 3)  1       1
+Total_time                                    -                                             328.771   -        -                  -       -
 </pre></div>
 </div>
 <div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-work-with-microtvm-micro-autotune-py">
diff --git a/docs/how_to/work_with_microtvm/sg_execution_times.html b/docs/how_to/work_with_microtvm/sg_execution_times.html
index 271d54648..08c12cec5 100644
--- a/docs/how_to/work_with_microtvm/sg_execution_times.html
+++ b/docs/how_to/work_with_microtvm/sg_execution_times.html
@@ -300,13 +300,13 @@
             
   <div class="section" id="computation-times">
 <span id="sphx-glr-how-to-work-with-microtvm-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>00:43.330</strong> total execution time for <strong>how_to_work_with_microtvm</strong> files:</p>
+<p><strong>00:46.129</strong> total execution time for <strong>how_to_work_with_microtvm</strong> files:</p>
 <ul class="simple">
-<li><p><strong>00:39.365</strong>: <a class="reference internal" href="micro_autotune.html#sphx-glr-how-to-work-with-microtvm-micro-autotune-py"><span class="std std-ref">Autotuning with microTVM</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_autotune.py</span></code>)</p></li>
-<li><p><strong>00:03.415</strong>: <a class="reference internal" href="micro_tflite.html#sphx-glr-how-to-work-with-microtvm-micro-tflite-py"><span class="std std-ref">microTVM with TFLite Models</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_tflite.py</span></code>)</p></li>
-<li><p><strong>00:00.190</strong>: <a class="reference internal" href="micro_tvmc.html#sphx-glr-how-to-work-with-microtvm-micro-tvmc-py"><span class="std std-ref">Executing a Tiny Model with TVMC Micro</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_tvmc.py</span></code>)</p></li>
-<li><p><strong>00:00.184</strong>: <a class="reference internal" href="micro_ethosu.html#sphx-glr-how-to-work-with-microtvm-micro-ethosu-py"><span class="std std-ref">Running TVM on bare metal Arm(R) Cortex(R)-M55 CPU and Ethos(TM)-U55 NPU</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_ethosu.py</span></code>)</p></li>
-<li><p><strong>00:00.176</strong>: <a class="reference internal" href="micro_reference_vm.html#sphx-glr-how-to-work-with-microtvm-micro-reference-vm-py"><span class="std std-ref">microTVM Reference Virtual Machines</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_reference_vm.py</span></code>)</p></li>
+<li><p><strong>00:41.923</strong>: <a class="reference internal" href="micro_autotune.html#sphx-glr-how-to-work-with-microtvm-micro-autotune-py"><span class="std std-ref">Autotuning with microTVM</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_autotune.py</span></code>)</p></li>
+<li><p><strong>00:03.605</strong>: <a class="reference internal" href="micro_tflite.html#sphx-glr-how-to-work-with-microtvm-micro-tflite-py"><span class="std std-ref">microTVM with TFLite Models</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_tflite.py</span></code>)</p></li>
+<li><p><strong>00:00.201</strong>: <a class="reference internal" href="micro_ethosu.html#sphx-glr-how-to-work-with-microtvm-micro-ethosu-py"><span class="std std-ref">Running TVM on bare metal Arm(R) Cortex(R)-M55 CPU and Ethos(TM)-U55 NPU</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_ethosu.py</span></code>)</p></li>
+<li><p><strong>00:00.201</strong>: <a class="reference internal" href="micro_reference_vm.html#sphx-glr-how-to-work-with-microtvm-micro-reference-vm-py"><span class="std std-ref">microTVM Reference Virtual Machines</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_reference_vm.py</span></code>)</p></li>
+<li><p><strong>00:00.199</strong>: <a class="reference internal" href="micro_tvmc.html#sphx-glr-how-to-work-with-microtvm-micro-tvmc-py"><span class="std std-ref">Executing a Tiny Model with TVMC Micro</span></a> (<code class="docutils literal notranslate"><span class="pre">micro_tvmc.py</span></code>)</p></li>
 </ul>
 </div>
 
diff --git a/docs/how_to/work_with_relay/sg_execution_times.html b/docs/how_to/work_with_relay/sg_execution_times.html
index a98edb802..819248e01 100644
--- a/docs/how_to/work_with_relay/sg_execution_times.html
+++ b/docs/how_to/work_with_relay/sg_execution_times.html
@@ -300,11 +300,11 @@
             
   <div class="section" id="computation-times">
 <span id="sphx-glr-how-to-work-with-relay-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>00:09.148</strong> total execution time for <strong>how_to_work_with_relay</strong> files:</p>
+<p><strong>00:09.486</strong> total execution time for <strong>how_to_work_with_relay</strong> files:</p>
 <ul class="simple">
-<li><p><strong>00:07.078</strong>: <a class="reference internal" href="using_external_lib.html#sphx-glr-how-to-work-with-relay-using-external-lib-py"><span class="std std-ref">Using External Libraries in Relay</span></a> (<code class="docutils literal notranslate"><span class="pre">using_external_lib.py</span></code>)</p></li>
-<li><p><strong>00:01.882</strong>: <a class="reference internal" href="build_gcn.html#sphx-glr-how-to-work-with-relay-build-gcn-py"><span class="std std-ref">Building a Graph Convolutional Network</span></a> (<code class="docutils literal notranslate"><span class="pre">build_gcn.py</span></code>)</p></li>
-<li><p><strong>00:00.188</strong>: <a class="reference internal" href="using_relay_viz.html#sphx-glr-how-to-work-with-relay-using-relay-viz-py"><span class="std std-ref">Use Relay Visualizer to Visualize Relay</span></a> (<code class="docutils literal notranslate"><span class="pre">using_relay_viz.py</span></code>)</p></li>
+<li><p><strong>00:07.191</strong>: <a class="reference internal" href="using_external_lib.html#sphx-glr-how-to-work-with-relay-using-external-lib-py"><span class="std std-ref">Using External Libraries in Relay</span></a> (<code class="docutils literal notranslate"><span class="pre">using_external_lib.py</span></code>)</p></li>
+<li><p><strong>00:02.079</strong>: <a class="reference internal" href="build_gcn.html#sphx-glr-how-to-work-with-relay-build-gcn-py"><span class="std std-ref">Building a Graph Convolutional Network</span></a> (<code class="docutils literal notranslate"><span class="pre">build_gcn.py</span></code>)</p></li>
+<li><p><strong>00:00.215</strong>: <a class="reference internal" href="using_relay_viz.html#sphx-glr-how-to-work-with-relay-using-relay-viz-py"><span class="std std-ref">Use Relay Visualizer to Visualize Relay</span></a> (<code class="docutils literal notranslate"><span class="pre">using_relay_viz.py</span></code>)</p></li>
 </ul>
 </div>
 
diff --git a/docs/how_to/work_with_schedules/sg_execution_times.html b/docs/how_to/work_with_schedules/sg_execution_times.html
index 7293a8100..7a01eb537 100644
--- a/docs/how_to/work_with_schedules/sg_execution_times.html
+++ b/docs/how_to/work_with_schedules/sg_execution_times.html
@@ -300,16 +300,16 @@
             
   <div class="section" id="computation-times">
 <span id="sphx-glr-how-to-work-with-schedules-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>00:05.384</strong> total execution time for <strong>how_to_work_with_schedules</strong> files:</p>
+<p><strong>00:05.609</strong> total execution time for <strong>how_to_work_with_schedules</strong> files:</p>
 <ul class="simple">
-<li><p><strong>00:02.025</strong>: <a class="reference internal" href="intrin_math.html#sphx-glr-how-to-work-with-schedules-intrin-math-py"><span class="std std-ref">Intrinsics and Math Functions</span></a> (<code class="docutils literal notranslate"><span class="pre">intrin_math.py</span></code>)</p></li>
-<li><p><strong>00:01.059</strong>: <a class="reference internal" href="tensorize.html#sphx-glr-how-to-work-with-schedules-tensorize-py"><span class="std std-ref">Use Tensorize to Leverage Hardware Intrinsics</span></a> (<code class="docutils literal notranslate"><span class="pre">tensorize.py</span></code>)</p></li>
-<li><p><strong>00:00.701</strong>: <a class="reference internal" href="reduction.html#sphx-glr-how-to-work-with-schedules-reduction-py"><span class="std std-ref">Reduction</span></a> (<code class="docutils literal notranslate"><span class="pre">reduction.py</span></code>)</p></li>
-<li><p><strong>00:00.690</strong>: <a class="reference internal" href="scan.html#sphx-glr-how-to-work-with-schedules-scan-py"><span class="std std-ref">Scan and Recurrent Kernel</span></a> (<code class="docutils literal notranslate"><span class="pre">scan.py</span></code>)</p></li>
-<li><p><strong>00:00.277</strong>: <a class="reference internal" href="extern_op.html#sphx-glr-how-to-work-with-schedules-extern-op-py"><span class="std std-ref">External Tensor Functions</span></a> (<code class="docutils literal notranslate"><span class="pre">extern_op.py</span></code>)</p></li>
-<li><p><strong>00:00.231</strong>: <a class="reference internal" href="schedule_primitives.html#sphx-glr-how-to-work-with-schedules-schedule-primitives-py"><span class="std std-ref">Schedule Primitives in TVM</span></a> (<code class="docutils literal notranslate"><span class="pre">schedule_primitives.py</span></code>)</p></li>
-<li><p><strong>00:00.207</strong>: <a class="reference internal" href="tedd.html#sphx-glr-how-to-work-with-schedules-tedd-py"><span class="std std-ref">Use Tensor Expression Debug Display (TEDD) for Visualization</span></a> (<code class="docutils literal notranslate"><span class="pre">tedd.py</span></code>)</p></li>
-<li><p><strong>00:00.194</strong>: <a class="reference internal" href="tuple_inputs.html#sphx-glr-how-to-work-with-schedules-tuple-inputs-py"><span class="std std-ref">Compute and Reduce with Tuple Inputs</span></a> (<code class="docutils literal notranslate"><span class="pre">tuple_inputs.py</span></code>)</p></li>
+<li><p><strong>00:02.080</strong>: <a class="reference internal" href="intrin_math.html#sphx-glr-how-to-work-with-schedules-intrin-math-py"><span class="std std-ref">Intrinsics and Math Functions</span></a> (<code class="docutils literal notranslate"><span class="pre">intrin_math.py</span></code>)</p></li>
+<li><p><strong>00:01.080</strong>: <a class="reference internal" href="tensorize.html#sphx-glr-how-to-work-with-schedules-tensorize-py"><span class="std std-ref">Use Tensorize to Leverage Hardware Intrinsics</span></a> (<code class="docutils literal notranslate"><span class="pre">tensorize.py</span></code>)</p></li>
+<li><p><strong>00:00.726</strong>: <a class="reference internal" href="reduction.html#sphx-glr-how-to-work-with-schedules-reduction-py"><span class="std std-ref">Reduction</span></a> (<code class="docutils literal notranslate"><span class="pre">reduction.py</span></code>)</p></li>
+<li><p><strong>00:00.715</strong>: <a class="reference internal" href="scan.html#sphx-glr-how-to-work-with-schedules-scan-py"><span class="std std-ref">Scan and Recurrent Kernel</span></a> (<code class="docutils literal notranslate"><span class="pre">scan.py</span></code>)</p></li>
+<li><p><strong>00:00.311</strong>: <a class="reference internal" href="extern_op.html#sphx-glr-how-to-work-with-schedules-extern-op-py"><span class="std std-ref">External Tensor Functions</span></a> (<code class="docutils literal notranslate"><span class="pre">extern_op.py</span></code>)</p></li>
+<li><p><strong>00:00.239</strong>: <a class="reference internal" href="schedule_primitives.html#sphx-glr-how-to-work-with-schedules-schedule-primitives-py"><span class="std std-ref">Schedule Primitives in TVM</span></a> (<code class="docutils literal notranslate"><span class="pre">schedule_primitives.py</span></code>)</p></li>
+<li><p><strong>00:00.235</strong>: <a class="reference internal" href="tedd.html#sphx-glr-how-to-work-with-schedules-tedd-py"><span class="std std-ref">Use Tensor Expression Debug Display (TEDD) for Visualization</span></a> (<code class="docutils literal notranslate"><span class="pre">tedd.py</span></code>)</p></li>
+<li><p><strong>00:00.223</strong>: <a class="reference internal" href="tuple_inputs.html#sphx-glr-how-to-work-with-schedules-tuple-inputs-py"><span class="std std-ref">Compute and Reduce with Tuple Inputs</span></a> (<code class="docutils literal notranslate"><span class="pre">tuple_inputs.py</span></code>)</p></li>
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diff --git a/docs/how_to/work_with_schedules/tensorize.html b/docs/how_to/work_with_schedules/tensorize.html
index 0fb69b4be..5259c8d8d 100644
--- a/docs/how_to/work_with_schedules/tensorize.html
+++ b/docs/how_to/work_with_schedules/tensorize.html
@@ -548,7 +548,7 @@ The importing needs to happen before the tensorized GEMV being executed.</p>
              B: Buffer(B_2: Pointer(float32), float32, [32768], []),
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   buffer_map = {A_1: A, B_1: B, C_1: C} {
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+  attr [IterVar(i: int32, (nullptr), &quot;DataPar&quot;, &quot;&quot;)] &quot;pragma_import_llvm&quot; = &quot;; ModuleID = &#39;/tmp/tmpzd4fx58c/input0.cc&#39;\nsource_filename = \&quot;/tmp/tmpzd4fx58c/input0.cc\&quot;\ntarget datalayout = \&quot;e-m:e-i64:64-f80:128-n8:16:32:64-S128\&quot;\ntarget triple = \&quot;x86_64-pc-linux-gnu\&quot;\n\n; Function Attrs: noinline nounwind optnone uwtable\ndefine dso_local i32 @gemv_update(float*, float*, float*, i32, i32, i32) #0 {\n  %7 = allo [...]
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diff --git a/docs/reference/api/python/auto_scheduler.html b/docs/reference/api/python/auto_scheduler.html
index 0aa40ce51..b773da553 100644
--- a/docs/reference/api/python/auto_scheduler.html
+++ b/docs/reference/api/python/auto_scheduler.html
@@ -1713,7 +1713,7 @@ Can be the a function or the function name.</p></li>
 
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 <dd><p>THIS API IS DEPRECATED.</p>
 <p>Run auto scheduling search for a task.</p>
 <dl class="field-list simple">
@@ -1750,7 +1750,7 @@ the initial naive schedule (state).</p>
 
 <dl class="py class">
 <dt class="sig sig-object py" id="tvm.auto_scheduler.SketchPolicy">
-<em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">tvm.auto_scheduler.</span></span><span class="sig-name descname"><span class="pre">SketchPolicy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">task</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">program_cost_model</span></span><span class="o"><span class="pre">=</span></span><span class="defau [...]
+<em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">tvm.auto_scheduler.</span></span><span class="sig-name descname"><span class="pre">SketchPolicy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">task</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">program_cost_model</span></span><span class="o"><span class="pre">=</span></span><span class="defau [...]
 <dd><p>The search policy that searches in a hierarchical search space defined by sketches.
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index c2501272f..8c85ce797 100644
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+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/rpc_server.ts#L42">rpc_server.ts:42</a></li>
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+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/rpc_server.ts#L63">rpc_server.ts:63</a></li>
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+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/rpc_server.ts#L49">rpc_server.ts:49</a></li>
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+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/rpc_server.ts#L57">rpc_server.ts:57</a></li>
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+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/memory.ts#L208">memory.ts:208</a></li>
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+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/memory.ts#L312">memory.ts:312</a></li>
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+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/memory.ts#L284">memory.ts:284</a></li>
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-									<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/memory.ts#L267">memory.ts:267</a></li>
+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/memory.ts#L267">memory.ts:267</a></li>
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 							<div class="tsd-comment tsd-typography">
@@ -373,7 +373,7 @@
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-									<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/memory.ts#L243">memory.ts:243</a></li>
+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/memory.ts#L243">memory.ts:243</a></li>
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 							<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4>
@@ -390,7 +390,7 @@
 						<li class="tsd-description">
 							<aside class="tsd-sources">
 								<ul>
-									<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/memory.ts#L321">memory.ts:321</a></li>
+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/memory.ts#L321">memory.ts:321</a></li>
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 							<div class="tsd-comment tsd-typography">
@@ -422,7 +422,7 @@
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-									<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/memory.ts#L252">memory.ts:252</a></li>
+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/memory.ts#L252">memory.ts:252</a></li>
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 							<div class="tsd-comment tsd-typography">
@@ -444,7 +444,7 @@
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-									<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/memory.ts#L359">memory.ts:359</a></li>
+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/memory.ts#L359">memory.ts:359</a></li>
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 							<h4 class="tsd-parameters-title">Parameters</h4>
@@ -470,7 +470,7 @@
 						<li class="tsd-description">
 							<aside class="tsd-sources">
 								<ul>
-									<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/memory.ts#L342">memory.ts:342</a></li>
+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/memory.ts#L342">memory.ts:342</a></li>
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 							<h4 class="tsd-parameters-title">Parameters</h4>
@@ -496,7 +496,7 @@
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 								<ul>
-									<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/memory.ts#L350">memory.ts:350</a></li>
+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/memory.ts#L350">memory.ts:350</a></li>
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 							<h4 class="tsd-parameters-title">Parameters</h4>
@@ -522,7 +522,7 @@
 						<li class="tsd-description">
 							<aside class="tsd-sources">
 								<ul>
-									<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/memory.ts#L326">memory.ts:326</a></li>
+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/memory.ts#L326">memory.ts:326</a></li>
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 							<h4 class="tsd-parameters-title">Parameters</h4>
@@ -548,7 +548,7 @@
 						<li class="tsd-description">
 							<aside class="tsd-sources">
 								<ul>
-									<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/memory.ts#L363">memory.ts:363</a></li>
+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/memory.ts#L363">memory.ts:363</a></li>
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 							<h4 class="tsd-parameters-title">Parameters</h4>
@@ -574,7 +574,7 @@
 						<li class="tsd-description">
 							<aside class="tsd-sources">
 								<ul>
-									<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/memory.ts#L346">memory.ts:346</a></li>
+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/memory.ts#L346">memory.ts:346</a></li>
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 							<h4 class="tsd-parameters-title">Parameters</h4>
@@ -600,7 +600,7 @@
 						<li class="tsd-description">
 							<aside class="tsd-sources">
 								<ul>
-									<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/memory.ts#L334">memory.ts:334</a></li>
+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/memory.ts#L334">memory.ts:334</a></li>
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 							<h4 class="tsd-parameters-title">Parameters</h4>
diff --git a/docs/reference/api/typedoc/classes/dldatatype.html b/docs/reference/api/typedoc/classes/dldatatype.html
index 838864c6a..7cb726b27 100644
--- a/docs/reference/api/typedoc/classes/dldatatype.html
+++ b/docs/reference/api/typedoc/classes/dldatatype.html
@@ -119,7 +119,7 @@
 						<li class="tsd-description">
 							<aside class="tsd-sources">
 								<ul>
-									<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L262">runtime.ts:262</a></li>
+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L262">runtime.ts:262</a></li>
 								</ul>
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 							<h4 class="tsd-parameters-title">Parameters</h4>
@@ -147,7 +147,7 @@
 					<div class="tsd-signature tsd-kind-icon">bits<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div>
 					<aside class="tsd-sources">
 						<ul>
-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L260">runtime.ts:260</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L260">runtime.ts:260</a></li>
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 					<div class="tsd-comment tsd-typography">
@@ -162,7 +162,7 @@
 					<div class="tsd-signature tsd-kind-icon">code<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div>
 					<aside class="tsd-sources">
 						<ul>
-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L258">runtime.ts:258</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L258">runtime.ts:258</a></li>
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 					<div class="tsd-comment tsd-typography">
@@ -177,7 +177,7 @@
 					<div class="tsd-signature tsd-kind-icon">lanes<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div>
 					<aside class="tsd-sources">
 						<ul>
-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L262">runtime.ts:262</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L262">runtime.ts:262</a></li>
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 					<div class="tsd-comment tsd-typography">
@@ -199,7 +199,7 @@
 						<li class="tsd-description">
 							<aside class="tsd-sources">
 								<ul>
-									<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L279">runtime.ts:279</a></li>
+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L279">runtime.ts:279</a></li>
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 							<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">number</span></h4>
@@ -216,7 +216,7 @@
 						<li class="tsd-description">
 							<aside class="tsd-sources">
 								<ul>
-									<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L270">runtime.ts:270</a></li>
+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L270">runtime.ts:270</a></li>
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 							<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">string</span></h4>
diff --git a/docs/reference/api/typedoc/classes/dldevice.html b/docs/reference/api/typedoc/classes/dldevice.html
index 97d3a3b5f..336808ebf 100644
--- a/docs/reference/api/typedoc/classes/dldevice.html
+++ b/docs/reference/api/typedoc/classes/dldevice.html
@@ -118,7 +118,7 @@
 						<li class="tsd-description">
 							<aside class="tsd-sources">
 								<ul>
-									<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L202">runtime.ts:202</a></li>
+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L202">runtime.ts:202</a></li>
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 							</aside>
 							<h4 class="tsd-parameters-title">Parameters</h4>
@@ -146,7 +146,7 @@
 					<div class="tsd-signature tsd-kind-icon">device<wbr>Id<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div>
 					<aside class="tsd-sources">
 						<ul>
-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L200">runtime.ts:200</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L200">runtime.ts:200</a></li>
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 					<div class="tsd-comment tsd-typography">
@@ -161,7 +161,7 @@
 					<div class="tsd-signature tsd-kind-icon">device<wbr>Type<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div>
 					<aside class="tsd-sources">
 						<ul>
-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L198">runtime.ts:198</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L198">runtime.ts:198</a></li>
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 					<div class="tsd-comment tsd-typography">
@@ -183,7 +183,7 @@
 						<li class="tsd-description">
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 								<ul>
-									<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L223">runtime.ts:223</a></li>
+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L223">runtime.ts:223</a></li>
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 							<div class="tsd-comment tsd-typography">
@@ -205,7 +205,7 @@
 						<li class="tsd-description">
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 								<ul>
-									<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L230">runtime.ts:230</a></li>
+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L230">runtime.ts:230</a></li>
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 							</aside>
 							<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">string</span></h4>
diff --git a/docs/reference/api/typedoc/classes/environment.html b/docs/reference/api/typedoc/classes/environment.html
index 033b42910..766460ffa 100644
--- a/docs/reference/api/typedoc/classes/environment.html
+++ b/docs/reference/api/typedoc/classes/environment.html
@@ -125,7 +125,7 @@
 						<li class="tsd-description">
 							<aside class="tsd-sources">
 								<ul>
-									<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/environment.ts#L86">environment.ts:86</a></li>
+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/environment.ts#L86">environment.ts:86</a></li>
 								</ul>
 							</aside>
 							<h4 class="tsd-parameters-title">Parameters</h4>
@@ -169,7 +169,7 @@
 					<aside class="tsd-sources">
 						<p>Implementation of <a href="../interfaces/libraryprovider.html">LibraryProvider</a>.<a href="../interfaces/libraryprovider.html#imports">imports</a></p>
 						<ul>
-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/environment.ts#L70">environment.ts:70</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/environment.ts#L70">environment.ts:70</a></li>
 						</ul>
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@@ -179,7 +179,7 @@
 					<div class="tsd-signature tsd-kind-icon">logger<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span>msg<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> =&gt; </span><span class="tsd-signature-type">void</span></div>
 					<aside class="tsd-sources">
 						<ul>
-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/environment.ts#L69">environment.ts:69</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/environment.ts#L69">environment.ts:69</a></li>
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 					</aside>
 					<div class="tsd-type-declaration">
@@ -210,7 +210,7 @@
 					<div class="tsd-signature tsd-kind-icon">packedCFunc<wbr>Table<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">Array</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">ctypes.FTVMWasmPackedCFunc</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">undefined</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> = [undefined,]</span></div>
 					<aside class="tsd-sources">
 						<ul>
-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/environment.ts#L78">environment.ts:78</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/environment.ts#L78">environment.ts:78</a></li>
 						</ul>
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 					<div class="tsd-comment tsd-typography">
@@ -228,7 +228,7 @@
 					<div class="tsd-signature tsd-kind-icon">packedCFunc<wbr>Table<wbr>Free<wbr>Id<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">Array</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> = []</span></div>
 					<aside class="tsd-sources">
 						<ul>
-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/environment.ts#L84">environment.ts:84</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/environment.ts#L84">environment.ts:84</a></li>
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 					<div class="tsd-comment tsd-typography">
@@ -250,7 +250,7 @@
 						<li class="tsd-description">
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 								<ul>
-									<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/environment.ts#L105">environment.ts:105</a></li>
+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/environment.ts#L105">environment.ts:105</a></li>
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diff --git a/docs/reference/api/typedoc/classes/ffilibrary.html b/docs/reference/api/typedoc/classes/ffilibrary.html
index 6d0c29202..195971de6 100644
--- a/docs/reference/api/typedoc/classes/ffilibrary.html
+++ b/docs/reference/api/typedoc/classes/ffilibrary.html
@@ -131,7 +131,7 @@
 						<li class="tsd-description">
 							<aside class="tsd-sources">
 								<ul>
-									<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L49">runtime.ts:49</a></li>
+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L49">runtime.ts:49</a></li>
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 							<h4 class="tsd-parameters-title">Parameters</h4>
@@ -156,7 +156,7 @@
 					<div class="tsd-signature tsd-kind-icon">exports<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">Record</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">Function</span><span class="tsd-signature-symbol">&gt;</span></div>
 					<aside class="tsd-sources">
 						<ul>
-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L46">runtime.ts:46</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L46">runtime.ts:46</a></li>
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@@ -166,7 +166,7 @@
 					<div class="tsd-signature tsd-kind-icon">memory<span class="tsd-signature-symbol">:</span> <a href="memory.html" class="tsd-signature-type">Memory</a></div>
 					<aside class="tsd-sources">
 						<ul>
-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L45">runtime.ts:45</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L45">runtime.ts:45</a></li>
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@@ -176,7 +176,7 @@
 					<div class="tsd-signature tsd-kind-icon">wasm32<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">boolean</span></div>
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 						<ul>
-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L44">runtime.ts:44</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L44">runtime.ts:44</a></li>
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@@ -186,7 +186,7 @@
 					<div class="tsd-signature tsd-kind-icon">webGPUContext<span class="tsd-signature-symbol">:</span> <a href="webgpucontext.html" class="tsd-signature-type">WebGPUContext</a></div>
 					<aside class="tsd-sources">
 						<ul>
-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L47">runtime.ts:47</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L47">runtime.ts:47</a></li>
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@@ -203,7 +203,7 @@
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-									<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L76">runtime.ts:76</a></li>
+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L76">runtime.ts:76</a></li>
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 							<h4 class="tsd-parameters-title">Parameters</h4>
@@ -226,7 +226,7 @@
 						<li class="tsd-description">
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-									<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L66">runtime.ts:66</a></li>
+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L66">runtime.ts:66</a></li>
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 							<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4>
@@ -243,7 +243,7 @@
 						<li class="tsd-description">
 							<aside class="tsd-sources">
 								<ul>
-									<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L84">runtime.ts:84</a></li>
+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L84">runtime.ts:84</a></li>
 								</ul>
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 							<h4 class="tsd-returns-title">Returns <a href="cachedcallstack.html" class="tsd-signature-type">CachedCallStack</a></h4>
@@ -260,7 +260,7 @@
 						<li class="tsd-description">
 							<aside class="tsd-sources">
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-									<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L95">runtime.ts:95</a></li>
+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L95">runtime.ts:95</a></li>
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 							<h4 class="tsd-parameters-title">Parameters</h4>
@@ -283,7 +283,7 @@
 						<li class="tsd-description">
 							<aside class="tsd-sources">
 								<ul>
-									<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L72">runtime.ts:72</a></li>
+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L72">runtime.ts:72</a></li>
 								</ul>
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 							<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">number</span></h4>
diff --git a/docs/reference/api/typedoc/classes/graphexecutor.html b/docs/reference/api/typedoc/classes/graphexecutor.html
index b0542d95c..5672ffd54 100644
--- a/docs/reference/api/typedoc/classes/graphexecutor.html
+++ b/docs/reference/api/typedoc/classes/graphexecutor.html
@@ -130,7 +130,7 @@
 						<li class="tsd-description">
 							<aside class="tsd-sources">
 								<ul>
-									<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L583">runtime.ts:583</a></li>
+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L583">runtime.ts:583</a></li>
 								</ul>
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 							<div class="tsd-comment tsd-typography">
@@ -162,7 +162,7 @@
 					<div class="tsd-signature tsd-kind-icon">module<span class="tsd-signature-symbol">:</span> <a href="module.html" class="tsd-signature-type">Module</a></div>
 					<aside class="tsd-sources">
 						<ul>
-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L579">runtime.ts:579</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L579">runtime.ts:579</a></li>
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@@ -179,7 +179,7 @@
 						<li class="tsd-description">
 							<aside class="tsd-sources">
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-									<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L654">runtime.ts:654</a></li>
+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L654">runtime.ts:654</a></li>
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 							<div class="tsd-comment tsd-typography">
@@ -224,7 +224,7 @@
 						<li class="tsd-description">
 							<aside class="tsd-sources">
 								<ul>
-									<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L597">runtime.ts:597</a></li>
+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L597">runtime.ts:597</a></li>
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 							<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4>
@@ -241,7 +241,7 @@
 						<li class="tsd-description">
 							<aside class="tsd-sources">
 								<ul>
-									<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L631">runtime.ts:631</a></li>
+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L631">runtime.ts:631</a></li>
 								</ul>
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 							<div class="tsd-comment tsd-typography">
@@ -279,7 +279,7 @@
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 							<aside class="tsd-sources">
 								<ul>
-									<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L644">runtime.ts:644</a></li>
+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L644">runtime.ts:644</a></li>
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 							<div class="tsd-comment tsd-typography">
@@ -310,7 +310,7 @@
 						<li class="tsd-description">
 							<aside class="tsd-sources">
 								<ul>
-									<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L621">runtime.ts:621</a></li>
+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L621">runtime.ts:621</a></li>
 								</ul>
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 							<div class="tsd-comment tsd-typography">
@@ -332,7 +332,7 @@
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 							<aside class="tsd-sources">
 								<ul>
-									<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L609">runtime.ts:609</a></li>
+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L609">runtime.ts:609</a></li>
 								</ul>
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 							<div class="tsd-comment tsd-typography">
diff --git a/docs/reference/api/typedoc/classes/instance.html b/docs/reference/api/typedoc/classes/instance.html
index b0786efd7..d61fcf041 100644
--- a/docs/reference/api/typedoc/classes/instance.html
+++ b/docs/reference/api/typedoc/classes/instance.html
@@ -139,7 +139,7 @@
 						<li class="tsd-description">
 							<aside class="tsd-sources">
 								<ul>
-									<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L692">runtime.ts:692</a></li>
+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L692">runtime.ts:692</a></li>
 								</ul>
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 							<div class="tsd-comment tsd-typography">
@@ -202,7 +202,7 @@
 					<div class="tsd-signature tsd-kind-icon">exports<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">Record</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">Function</span><span class="tsd-signature-symbol">&gt;</span></div>
 					<aside class="tsd-sources">
 						<ul>
-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L684">runtime.ts:684</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L684">runtime.ts:684</a></li>
 						</ul>
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@@ -212,7 +212,7 @@
 					<div class="tsd-signature tsd-kind-icon">memory<span class="tsd-signature-symbol">:</span> <a href="memory.html" class="tsd-signature-type">Memory</a></div>
 					<aside class="tsd-sources">
 						<ul>
-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L683">runtime.ts:683</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L683">runtime.ts:683</a></li>
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@@ -229,7 +229,7 @@
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 							<aside class="tsd-sources">
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-									<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L932">runtime.ts:932</a></li>
+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L932">runtime.ts:932</a></li>
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 							<div class="tsd-comment tsd-typography">
@@ -260,7 +260,7 @@
 						<li class="tsd-description">
 							<aside class="tsd-sources">
 								<ul>
-									<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L994">runtime.ts:994</a></li>
+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L994">runtime.ts:994</a></li>
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 							<div class="tsd-comment tsd-typography">
@@ -303,7 +303,7 @@
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 							<aside class="tsd-sources">
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-									<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L924">runtime.ts:924</a></li>
+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L924">runtime.ts:924</a></li>
 								</ul>
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 							<div class="tsd-comment tsd-typography">
@@ -341,7 +341,7 @@
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 							<aside class="tsd-sources">
 								<ul>
-									<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L732">runtime.ts:732</a></li>
+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L732">runtime.ts:732</a></li>
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 							<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4>
@@ -358,7 +358,7 @@
 						<li class="tsd-description">
 							<aside class="tsd-sources">
 								<ul>
-									<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L952">runtime.ts:952</a></li>
+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L952">runtime.ts:952</a></li>
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 							<div class="tsd-comment tsd-typography">
@@ -402,7 +402,7 @@
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-									<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L816">runtime.ts:816</a></li>
+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L816">runtime.ts:816</a></li>
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 							<div class="tsd-comment tsd-typography">
@@ -434,7 +434,7 @@
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-									<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L1033">runtime.ts:1033</a></li>
+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L1033">runtime.ts:1033</a></li>
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 							<div class="tsd-comment tsd-typography">
@@ -465,7 +465,7 @@
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-									<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L846">runtime.ts:846</a></li>
+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L846">runtime.ts:846</a></li>
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 							<div class="tsd-comment tsd-typography">
@@ -497,7 +497,7 @@
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 							<aside class="tsd-sources">
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-									<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L750">runtime.ts:750</a></li>
+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L750">runtime.ts:750</a></li>
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 							<div class="tsd-comment tsd-typography">
@@ -520,7 +520,7 @@
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 							<aside class="tsd-sources">
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-									<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L1013">runtime.ts:1013</a></li>
+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L1013">runtime.ts:1013</a></li>
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 							<div class="tsd-comment tsd-typography">
@@ -568,7 +568,7 @@
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-									<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L789">runtime.ts:789</a></li>
+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L789">runtime.ts:789</a></li>
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 							<div class="tsd-comment tsd-typography">
@@ -608,7 +608,7 @@
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-									<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L914">runtime.ts:914</a></li>
+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L914">runtime.ts:914</a></li>
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 							<div class="tsd-comment tsd-typography">
@@ -646,7 +646,7 @@
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-									<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L1134">runtime.ts:1134</a></li>
+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L1134">runtime.ts:1134</a></li>
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 							<div class="tsd-comment tsd-typography">
@@ -698,7 +698,7 @@
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-									<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L740">runtime.ts:740</a></li>
+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L740">runtime.ts:740</a></li>
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 							<div class="tsd-comment tsd-typography">
@@ -722,7 +722,7 @@
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-									<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L868">runtime.ts:868</a></li>
+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L868">runtime.ts:868</a></li>
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 							<div class="tsd-comment tsd-typography">
@@ -754,7 +754,7 @@
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-									<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L857">runtime.ts:857</a></li>
+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L857">runtime.ts:857</a></li>
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 							<div class="tsd-comment tsd-typography">
@@ -786,7 +786,7 @@
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-									<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L940">runtime.ts:940</a></li>
+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L940">runtime.ts:940</a></li>
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 							<div class="tsd-comment tsd-typography">
diff --git a/docs/reference/api/typedoc/classes/memory.html b/docs/reference/api/typedoc/classes/memory.html
index 075ead122..b99804a46 100644
--- a/docs/reference/api/typedoc/classes/memory.html
+++ b/docs/reference/api/typedoc/classes/memory.html
@@ -130,7 +130,7 @@
 						<li class="tsd-description">
 							<aside class="tsd-sources">
 								<ul>
-									<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/memory.ts#L40">memory.ts:40</a></li>
+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/memory.ts#L40">memory.ts:40</a></li>
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 							<h4 class="tsd-parameters-title">Parameters</h4>
@@ -152,7 +152,7 @@
 					<div class="tsd-signature tsd-kind-icon">memory<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">Memory</span></div>
 					<aside class="tsd-sources">
 						<ul>
-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/memory.ts#L32">memory.ts:32</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/memory.ts#L32">memory.ts:32</a></li>
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@@ -162,7 +162,7 @@
 					<div class="tsd-signature tsd-kind-icon">wasm32<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">boolean</span><span class="tsd-signature-symbol"> = true</span></div>
 					<aside class="tsd-sources">
 						<ul>
-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/memory.ts#L33">memory.ts:33</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/memory.ts#L33">memory.ts:33</a></li>
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@@ -179,7 +179,7 @@
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-									<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/memory.ts#L154">memory.ts:154</a></li>
+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/memory.ts#L154">memory.ts:154</a></li>
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 							<div class="tsd-comment tsd-typography">
@@ -210,7 +210,7 @@
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-									<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/memory.ts#L90">memory.ts:90</a></li>
+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/memory.ts#L90">memory.ts:90</a></li>
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 							<h4 class="tsd-parameters-title">Parameters</h4>
@@ -233,7 +233,7 @@
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-									<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/memory.ts#L97">memory.ts:97</a></li>
+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/memory.ts#L97">memory.ts:97</a></li>
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 							<h4 class="tsd-parameters-title">Parameters</h4>
@@ -256,7 +256,7 @@
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 							<aside class="tsd-sources">
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-									<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/memory.ts#L74">memory.ts:74</a></li>
+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/memory.ts#L74">memory.ts:74</a></li>
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 							<h4 class="tsd-parameters-title">Parameters</h4>
@@ -279,7 +279,7 @@
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-									<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/memory.ts#L81">memory.ts:81</a></li>
+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/memory.ts#L81">memory.ts:81</a></li>
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@@ -302,7 +302,7 @@
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-									<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/memory.ts#L104">memory.ts:104</a></li>
+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/memory.ts#L104">memory.ts:104</a></li>
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@@ -325,7 +325,7 @@
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-									<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/memory.ts#L132">memory.ts:132</a></li>
+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/memory.ts#L132">memory.ts:132</a></li>
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@@ -362,7 +362,7 @@
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+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/memory.ts#L145">memory.ts:145</a></li>
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@@ -393,7 +393,7 @@
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+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/memory.ts#L60">memory.ts:60</a></li>
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 							<h4 class="tsd-parameters-title">Parameters</h4>
@@ -416,7 +416,7 @@
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+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/memory.ts#L67">memory.ts:67</a></li>
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 							<h4 class="tsd-parameters-title">Parameters</h4>
@@ -439,7 +439,7 @@
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+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/memory.ts#L53">memory.ts:53</a></li>
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 							<h4 class="tsd-parameters-title">Parameters</h4>
@@ -462,7 +462,7 @@
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+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/memory.ts#L114">memory.ts:114</a></li>
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 							<h4 class="tsd-parameters-title">Parameters</h4>
@@ -485,7 +485,7 @@
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-									<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/memory.ts#L124">memory.ts:124</a></li>
+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/memory.ts#L124">memory.ts:124</a></li>
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 							<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">number</span></h4>
@@ -502,7 +502,7 @@
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-									<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/memory.ts#L175">memory.ts:175</a></li>
+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/memory.ts#L175">memory.ts:175</a></li>
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diff --git a/docs/reference/api/typedoc/classes/module.html b/docs/reference/api/typedoc/classes/module.html
index 1ca6ce1cc..efda6e27b 100644
--- a/docs/reference/api/typedoc/classes/module.html
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@@ -124,7 +124,7 @@
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-									<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L504">runtime.ts:504</a></li>
+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L504">runtime.ts:504</a></li>
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 							<h4 class="tsd-parameters-title">Parameters</h4>
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 						<ul>
-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L502">runtime.ts:502</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L502">runtime.ts:502</a></li>
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@@ -187,7 +187,7 @@
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+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L516">runtime.ts:516</a></li>
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 							<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4>
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-									<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L530">runtime.ts:530</a></li>
+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L530">runtime.ts:530</a></li>
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@@ -236,7 +236,7 @@
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+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L561">runtime.ts:561</a></li>
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diff --git a/docs/reference/api/typedoc/classes/ndarray.html b/docs/reference/api/typedoc/classes/ndarray.html
index 19639f44d..5705c15f3 100644
--- a/docs/reference/api/typedoc/classes/ndarray.html
+++ b/docs/reference/api/typedoc/classes/ndarray.html
@@ -130,7 +130,7 @@
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-									<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L304">runtime.ts:304</a></li>
+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L304">runtime.ts:304</a></li>
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 							<h4 class="tsd-parameters-title">Parameters</h4>
@@ -158,7 +158,7 @@
 					<div class="tsd-signature tsd-kind-icon">device<span class="tsd-signature-symbol">:</span> <a href="dldevice.html" class="tsd-signature-type">DLDevice</a></div>
 					<aside class="tsd-sources">
 						<ul>
-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L297">runtime.ts:297</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L297">runtime.ts:297</a></li>
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@@ -173,7 +173,7 @@
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 					<aside class="tsd-sources">
 						<ul>
-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L293">runtime.ts:293</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L293">runtime.ts:293</a></li>
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 					<div class="tsd-comment tsd-typography">
@@ -188,7 +188,7 @@
 					<div class="tsd-signature tsd-kind-icon">handle<span class="tsd-signature-symbol">:</span> <a href="../index.html#pointer" class="tsd-signature-type">Pointer</a></div>
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 						<ul>
-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L289">runtime.ts:289</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L289">runtime.ts:289</a></li>
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 					<div class="tsd-comment tsd-typography">
@@ -203,7 +203,7 @@
 					<div class="tsd-signature tsd-kind-icon">ndim<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div>
 					<aside class="tsd-sources">
 						<ul>
-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L291">runtime.ts:291</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L291">runtime.ts:291</a></li>
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@@ -218,7 +218,7 @@
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 					<aside class="tsd-sources">
 						<ul>
-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L295">runtime.ts:295</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L295">runtime.ts:295</a></li>
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@@ -240,7 +240,7 @@
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-									<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L370">runtime.ts:370</a></li>
+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L370">runtime.ts:370</a></li>
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@@ -273,7 +273,7 @@
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-									<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L414">runtime.ts:414</a></li>
+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L414">runtime.ts:414</a></li>
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@@ -305,7 +305,7 @@
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-									<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L355">runtime.ts:355</a></li>
+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L355">runtime.ts:355</a></li>
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 							<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4>
@@ -322,7 +322,7 @@
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-									<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L474">runtime.ts:474</a></li>
+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L474">runtime.ts:474</a></li>
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 							<div class="tsd-comment tsd-typography">
@@ -346,7 +346,7 @@
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-									<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L443">runtime.ts:443</a></li>
+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L443">runtime.ts:443</a></li>
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 							<div class="tsd-comment tsd-typography">
diff --git a/docs/reference/api/typedoc/classes/packedfunccell.html b/docs/reference/api/typedoc/classes/packedfunccell.html
index 7a4c997c7..dd11a266b 100644
--- a/docs/reference/api/typedoc/classes/packedfunccell.html
+++ b/docs/reference/api/typedoc/classes/packedfunccell.html
@@ -122,7 +122,7 @@
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-									<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L158">runtime.ts:158</a></li>
+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L158">runtime.ts:158</a></li>
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 							<h4 class="tsd-parameters-title">Parameters</h4>
@@ -147,7 +147,7 @@
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 						<ul>
-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L157">runtime.ts:157</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L157">runtime.ts:157</a></li>
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@@ -164,7 +164,7 @@
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+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L165">runtime.ts:165</a></li>
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 							<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4>
diff --git a/docs/reference/api/typedoc/classes/rpcserver.html b/docs/reference/api/typedoc/classes/rpcserver.html
index 6b6b9baf5..408d85fc7 100644
--- a/docs/reference/api/typedoc/classes/rpcserver.html
+++ b/docs/reference/api/typedoc/classes/rpcserver.html
@@ -115,7 +115,7 @@
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-									<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/rpc_server.ts#L92">rpc_server.ts:92</a></li>
+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/rpc_server.ts#L92">rpc_server.ts:92</a></li>
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 							<h4 class="tsd-parameters-title">Parameters</h4>
@@ -176,7 +176,7 @@
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 					<aside class="tsd-sources">
 						<ul>
-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/rpc_server.ts#L82">rpc_server.ts:82</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/rpc_server.ts#L82">rpc_server.ts:82</a></li>
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 					<div class="tsd-type-declaration">
@@ -201,7 +201,7 @@
 					<div class="tsd-signature tsd-kind-icon">key<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">string</span></div>
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 						<ul>
-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/rpc_server.ts#L78">rpc_server.ts:78</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/rpc_server.ts#L78">rpc_server.ts:78</a></li>
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@@ -211,7 +211,7 @@
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-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/rpc_server.ts#L81">rpc_server.ts:81</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/rpc_server.ts#L81">rpc_server.ts:81</a></li>
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 					<div class="tsd-type-declaration">
@@ -242,7 +242,7 @@
 					<div class="tsd-signature tsd-kind-icon">socket<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">WebSocket</span></div>
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-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/rpc_server.ts#L79">rpc_server.ts:79</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/rpc_server.ts#L79">rpc_server.ts:79</a></li>
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@@ -252,7 +252,7 @@
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 					<aside class="tsd-sources">
 						<ul>
-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/rpc_server.ts#L80">rpc_server.ts:80</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/rpc_server.ts#L80">rpc_server.ts:80</a></li>
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@@ -262,7 +262,7 @@
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-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/rpc_server.ts#L77">rpc_server.ts:77</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/rpc_server.ts#L77">rpc_server.ts:77</a></li>
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diff --git a/docs/reference/api/typedoc/classes/scalar.html b/docs/reference/api/typedoc/classes/scalar.html
index 5a7b60bc6..fb91a5e9d 100644
--- a/docs/reference/api/typedoc/classes/scalar.html
+++ b/docs/reference/api/typedoc/classes/scalar.html
@@ -112,7 +112,7 @@
 						<li class="tsd-description">
 							<aside class="tsd-sources">
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-									<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L145">runtime.ts:145</a></li>
+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L145">runtime.ts:145</a></li>
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 							<h4 class="tsd-parameters-title">Parameters</h4>
@@ -137,7 +137,7 @@
 					<div class="tsd-signature tsd-kind-icon">dtype<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">string</span></div>
 					<aside class="tsd-sources">
 						<ul>
-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L145">runtime.ts:145</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L145">runtime.ts:145</a></li>
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 					<div class="tsd-comment tsd-typography">
@@ -152,7 +152,7 @@
 					<div class="tsd-signature tsd-kind-icon">value<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div>
 					<aside class="tsd-sources">
 						<ul>
-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L143">runtime.ts:143</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L143">runtime.ts:143</a></li>
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diff --git a/docs/reference/api/typedoc/classes/webgpucontext.html b/docs/reference/api/typedoc/classes/webgpucontext.html
index a0426c9cc..1d1b1c01e 100644
--- a/docs/reference/api/typedoc/classes/webgpucontext.html
+++ b/docs/reference/api/typedoc/classes/webgpucontext.html
@@ -120,7 +120,7 @@
 						<li class="tsd-description">
 							<aside class="tsd-sources">
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-									<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/webgpu.ts#L57">webgpu.ts:57</a></li>
+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/webgpu.ts#L57">webgpu.ts:57</a></li>
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 							<h4 class="tsd-parameters-title">Parameters</h4>
@@ -145,7 +145,7 @@
 					<div class="tsd-signature tsd-kind-icon">device<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">GPUDevice</span></div>
 					<aside class="tsd-sources">
 						<ul>
-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/webgpu.ts#L50">webgpu.ts:50</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/webgpu.ts#L50">webgpu.ts:50</a></li>
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@@ -155,7 +155,7 @@
 					<div class="tsd-signature tsd-kind-icon">memory<span class="tsd-signature-symbol">:</span> <a href="memory.html" class="tsd-signature-type">Memory</a></div>
 					<aside class="tsd-sources">
 						<ul>
-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/webgpu.ts#L51">webgpu.ts:51</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/webgpu.ts#L51">webgpu.ts:51</a></li>
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@@ -172,7 +172,7 @@
 						<li class="tsd-description">
 							<aside class="tsd-sources">
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-									<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/webgpu.ts#L84">webgpu.ts:84</a></li>
+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/webgpu.ts#L84">webgpu.ts:84</a></li>
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 							<div class="tsd-comment tsd-typography">
@@ -209,7 +209,7 @@
 						<li class="tsd-description">
 							<aside class="tsd-sources">
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-									<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/webgpu.ts#L170">webgpu.ts:170</a></li>
+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/webgpu.ts#L170">webgpu.ts:170</a></li>
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 							<div class="tsd-comment tsd-typography">
@@ -238,7 +238,7 @@
 						<li class="tsd-description">
 							<aside class="tsd-sources">
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-									<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/webgpu.ts#L67">webgpu.ts:67</a></li>
+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/webgpu.ts#L67">webgpu.ts:67</a></li>
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 							<div class="tsd-comment tsd-typography">
diff --git a/docs/reference/api/typedoc/enums/argtypecode.html b/docs/reference/api/typedoc/enums/argtypecode.html
index a5d0a19c1..e4bd955f8 100644
--- a/docs/reference/api/typedoc/enums/argtypecode.html
+++ b/docs/reference/api/typedoc/enums/argtypecode.html
@@ -106,7 +106,7 @@
 					<div class="tsd-signature tsd-kind-icon">DLDevice<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = 6</span></div>
 					<aside class="tsd-sources">
 						<ul>
-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/ctypes.ts#L220">ctypes.ts:220</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/ctypes.ts#L220">ctypes.ts:220</a></li>
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@@ -116,7 +116,7 @@
 					<div class="tsd-signature tsd-kind-icon">Float<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = 2</span></div>
 					<aside class="tsd-sources">
 						<ul>
-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/ctypes.ts#L216">ctypes.ts:216</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/ctypes.ts#L216">ctypes.ts:216</a></li>
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@@ -126,7 +126,7 @@
 					<div class="tsd-signature tsd-kind-icon">Int<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = 0</span></div>
 					<aside class="tsd-sources">
 						<ul>
-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/ctypes.ts#L214">ctypes.ts:214</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/ctypes.ts#L214">ctypes.ts:214</a></li>
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@@ -136,7 +136,7 @@
 					<div class="tsd-signature tsd-kind-icon">Null<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = 4</span></div>
 					<aside class="tsd-sources">
 						<ul>
-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/ctypes.ts#L218">ctypes.ts:218</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/ctypes.ts#L218">ctypes.ts:218</a></li>
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@@ -146,7 +146,7 @@
 					<div class="tsd-signature tsd-kind-icon">TVMBytes<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = 12</span></div>
 					<aside class="tsd-sources">
 						<ul>
-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/ctypes.ts#L226">ctypes.ts:226</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/ctypes.ts#L226">ctypes.ts:226</a></li>
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@@ -156,7 +156,7 @@
 					<div class="tsd-signature tsd-kind-icon">TVMDLTensor<wbr>Handle<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = 7</span></div>
 					<aside class="tsd-sources">
 						<ul>
-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/ctypes.ts#L221">ctypes.ts:221</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/ctypes.ts#L221">ctypes.ts:221</a></li>
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@@ -166,7 +166,7 @@
 					<div class="tsd-signature tsd-kind-icon">TVMData<wbr>Type<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = 5</span></div>
 					<aside class="tsd-sources">
 						<ul>
-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/ctypes.ts#L219">ctypes.ts:219</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/ctypes.ts#L219">ctypes.ts:219</a></li>
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@@ -176,7 +176,7 @@
 					<div class="tsd-signature tsd-kind-icon">TVMModule<wbr>Handle<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = 9</span></div>
 					<aside class="tsd-sources">
 						<ul>
-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/ctypes.ts#L223">ctypes.ts:223</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/ctypes.ts#L223">ctypes.ts:223</a></li>
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@@ -186,7 +186,7 @@
 					<div class="tsd-signature tsd-kind-icon">TVMNDArray<wbr>Handle<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = 13</span></div>
 					<aside class="tsd-sources">
 						<ul>
-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/ctypes.ts#L227">ctypes.ts:227</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/ctypes.ts#L227">ctypes.ts:227</a></li>
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@@ -196,7 +196,7 @@
 					<div class="tsd-signature tsd-kind-icon">TVMObject<wbr>Handle<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = 8</span></div>
 					<aside class="tsd-sources">
 						<ul>
-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/ctypes.ts#L222">ctypes.ts:222</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/ctypes.ts#L222">ctypes.ts:222</a></li>
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@@ -206,7 +206,7 @@
 					<div class="tsd-signature tsd-kind-icon">TVMObjectRValue<wbr>Ref<wbr>Arg<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = 14</span></div>
 					<aside class="tsd-sources">
 						<ul>
-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/ctypes.ts#L228">ctypes.ts:228</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/ctypes.ts#L228">ctypes.ts:228</a></li>
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@@ -216,7 +216,7 @@
 					<div class="tsd-signature tsd-kind-icon">TVMOpaque<wbr>Handle<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = 3</span></div>
 					<aside class="tsd-sources">
 						<ul>
-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/ctypes.ts#L217">ctypes.ts:217</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/ctypes.ts#L217">ctypes.ts:217</a></li>
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@@ -226,7 +226,7 @@
 					<div class="tsd-signature tsd-kind-icon">TVMPacked<wbr>Func<wbr>Handle<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = 10</span></div>
 					<aside class="tsd-sources">
 						<ul>
-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/ctypes.ts#L224">ctypes.ts:224</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/ctypes.ts#L224">ctypes.ts:224</a></li>
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@@ -236,7 +236,7 @@
 					<div class="tsd-signature tsd-kind-icon">TVMStr<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = 11</span></div>
 					<aside class="tsd-sources">
 						<ul>
-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/ctypes.ts#L225">ctypes.ts:225</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/ctypes.ts#L225">ctypes.ts:225</a></li>
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@@ -246,7 +246,7 @@
 					<div class="tsd-signature tsd-kind-icon">UInt<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = 1</span></div>
 					<aside class="tsd-sources">
 						<ul>
-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/ctypes.ts#L215">ctypes.ts:215</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/ctypes.ts#L215">ctypes.ts:215</a></li>
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diff --git a/docs/reference/api/typedoc/enums/aynccallbackcode.html b/docs/reference/api/typedoc/enums/aynccallbackcode.html
index 8a3bf32e3..9621fd1d5 100644
--- a/docs/reference/api/typedoc/enums/aynccallbackcode.html
+++ b/docs/reference/api/typedoc/enums/aynccallbackcode.html
@@ -93,7 +93,7 @@
 					<div class="tsd-signature tsd-kind-icon">k<wbr>Exception<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = 5</span></div>
 					<aside class="tsd-sources">
 						<ul>
-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L676">runtime.ts:676</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L676">runtime.ts:676</a></li>
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@@ -103,7 +103,7 @@
 					<div class="tsd-signature tsd-kind-icon">k<wbr>Return<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = 4</span></div>
 					<aside class="tsd-sources">
 						<ul>
-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L675">runtime.ts:675</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L675">runtime.ts:675</a></li>
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diff --git a/docs/reference/api/typedoc/enums/dldatatypecode.html b/docs/reference/api/typedoc/enums/dldatatypecode.html
index bd5376a0b..a1f2ad211 100644
--- a/docs/reference/api/typedoc/enums/dldatatypecode.html
+++ b/docs/reference/api/typedoc/enums/dldatatypecode.html
@@ -95,7 +95,7 @@
 					<div class="tsd-signature tsd-kind-icon">Float<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = 2</span></div>
 					<aside class="tsd-sources">
 						<ul>
-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L242">runtime.ts:242</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L242">runtime.ts:242</a></li>
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@@ -105,7 +105,7 @@
 					<div class="tsd-signature tsd-kind-icon">Int<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = 0</span></div>
 					<aside class="tsd-sources">
 						<ul>
-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L240">runtime.ts:240</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L240">runtime.ts:240</a></li>
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@@ -115,7 +115,7 @@
 					<div class="tsd-signature tsd-kind-icon">Opaque<wbr>Handle<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = 3</span></div>
 					<aside class="tsd-sources">
 						<ul>
-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L243">runtime.ts:243</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L243">runtime.ts:243</a></li>
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@@ -125,7 +125,7 @@
 					<div class="tsd-signature tsd-kind-icon">UInt<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = 1</span></div>
 					<aside class="tsd-sources">
 						<ul>
-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L241">runtime.ts:241</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L241">runtime.ts:241</a></li>
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diff --git a/docs/reference/api/typedoc/enums/rpcserverstate.html b/docs/reference/api/typedoc/enums/rpcserverstate.html
index 7aefc624c..ff43562f0 100644
--- a/docs/reference/api/typedoc/enums/rpcserverstate.html
+++ b/docs/reference/api/typedoc/enums/rpcserverstate.html
@@ -90,7 +90,7 @@
 					<div class="tsd-signature tsd-kind-icon">Init<wbr>Header<span class="tsd-signature-symbol">:</span></div>
 					<aside class="tsd-sources">
 						<ul>
-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/rpc_server.ts#L27">rpc_server.ts:27</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/rpc_server.ts#L27">rpc_server.ts:27</a></li>
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@@ -100,7 +100,7 @@
 					<div class="tsd-signature tsd-kind-icon">Init<wbr>Header<wbr>Key<span class="tsd-signature-symbol">:</span></div>
 					<aside class="tsd-sources">
 						<ul>
-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/rpc_server.ts#L28">rpc_server.ts:28</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/rpc_server.ts#L28">rpc_server.ts:28</a></li>
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@@ -110,7 +110,7 @@
 					<div class="tsd-signature tsd-kind-icon">Init<wbr>Server<span class="tsd-signature-symbol">:</span></div>
 					<aside class="tsd-sources">
 						<ul>
-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/rpc_server.ts#L29">rpc_server.ts:29</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/rpc_server.ts#L29">rpc_server.ts:29</a></li>
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@@ -120,7 +120,7 @@
 					<div class="tsd-signature tsd-kind-icon">Receive<wbr>Packet<wbr>Body<span class="tsd-signature-symbol">:</span></div>
 					<aside class="tsd-sources">
 						<ul>
-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/rpc_server.ts#L32">rpc_server.ts:32</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/rpc_server.ts#L32">rpc_server.ts:32</a></li>
 						</ul>
 					</aside>
 				</section>
@@ -130,7 +130,7 @@
 					<div class="tsd-signature tsd-kind-icon">Receive<wbr>Packet<wbr>Header<span class="tsd-signature-symbol">:</span></div>
 					<aside class="tsd-sources">
 						<ul>
-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/rpc_server.ts#L31">rpc_server.ts:31</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/rpc_server.ts#L31">rpc_server.ts:31</a></li>
 						</ul>
 					</aside>
 				</section>
@@ -140,7 +140,7 @@
 					<div class="tsd-signature tsd-kind-icon">Wait<wbr>For<wbr>Callback<span class="tsd-signature-symbol">:</span></div>
 					<aside class="tsd-sources">
 						<ul>
-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/rpc_server.ts#L30">rpc_server.ts:30</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/rpc_server.ts#L30">rpc_server.ts:30</a></li>
 						</ul>
 					</aside>
 				</section>
diff --git a/docs/reference/api/typedoc/enums/sizeof.html b/docs/reference/api/typedoc/enums/sizeof.html
index 09f6d4d40..951ad2ca3 100644
--- a/docs/reference/api/typedoc/enums/sizeof.html
+++ b/docs/reference/api/typedoc/enums/sizeof.html
@@ -100,7 +100,7 @@
 					<div class="tsd-signature tsd-kind-icon">DLData<wbr>Type<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = I32</span></div>
 					<aside class="tsd-sources">
 						<ul>
-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/ctypes.ts#L206">ctypes.ts:206</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/ctypes.ts#L206">ctypes.ts:206</a></li>
 						</ul>
 					</aside>
 				</section>
@@ -110,7 +110,7 @@
 					<div class="tsd-signature tsd-kind-icon">DLDevice<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = I32 + I32</span></div>
 					<aside class="tsd-sources">
 						<ul>
-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/ctypes.ts#L207">ctypes.ts:207</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/ctypes.ts#L207">ctypes.ts:207</a></li>
 						</ul>
 					</aside>
 				</section>
@@ -120,7 +120,7 @@
 					<div class="tsd-signature tsd-kind-icon">F32<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = 4</span></div>
 					<aside class="tsd-sources">
 						<ul>
-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/ctypes.ts#L203">ctypes.ts:203</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/ctypes.ts#L203">ctypes.ts:203</a></li>
 						</ul>
 					</aside>
 				</section>
@@ -130,7 +130,7 @@
 					<div class="tsd-signature tsd-kind-icon">F64<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = 8</span></div>
 					<aside class="tsd-sources">
 						<ul>
-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/ctypes.ts#L204">ctypes.ts:204</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/ctypes.ts#L204">ctypes.ts:204</a></li>
 						</ul>
 					</aside>
 				</section>
@@ -140,7 +140,7 @@
 					<div class="tsd-signature tsd-kind-icon">I32<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = 4</span></div>
 					<aside class="tsd-sources">
 						<ul>
-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/ctypes.ts#L201">ctypes.ts:201</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/ctypes.ts#L201">ctypes.ts:201</a></li>
 						</ul>
 					</aside>
 				</section>
@@ -150,7 +150,7 @@
 					<div class="tsd-signature tsd-kind-icon">I64<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = 8</span></div>
 					<aside class="tsd-sources">
 						<ul>
-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/ctypes.ts#L202">ctypes.ts:202</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/ctypes.ts#L202">ctypes.ts:202</a></li>
 						</ul>
 					</aside>
 				</section>
@@ -160,7 +160,7 @@
 					<div class="tsd-signature tsd-kind-icon">TVMValue<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = 8</span></div>
 					<aside class="tsd-sources">
 						<ul>
-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/ctypes.ts#L205">ctypes.ts:205</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/ctypes.ts#L205">ctypes.ts:205</a></li>
 						</ul>
 					</aside>
 				</section>
@@ -170,7 +170,7 @@
 					<div class="tsd-signature tsd-kind-icon">U16<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = 2</span></div>
 					<aside class="tsd-sources">
 						<ul>
-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/ctypes.ts#L200">ctypes.ts:200</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/ctypes.ts#L200">ctypes.ts:200</a></li>
 						</ul>
 					</aside>
 				</section>
@@ -180,7 +180,7 @@
 					<div class="tsd-signature tsd-kind-icon">U8<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol"> = 1</span></div>
 					<aside class="tsd-sources">
 						<ul>
-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/ctypes.ts#L199">ctypes.ts:199</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/ctypes.ts#L199">ctypes.ts:199</a></li>
 						</ul>
 					</aside>
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diff --git a/docs/reference/api/typedoc/index.html b/docs/reference/api/typedoc/index.html
index 59b3414dd..51f3d6203 100644
--- a/docs/reference/api/typedoc/index.html
+++ b/docs/reference/api/typedoc/index.html
@@ -174,7 +174,7 @@
 					<div class="tsd-signature tsd-kind-icon">FTVMArray<wbr>Alloc<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span>shape<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a>, ndim<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span>, dtypeCode<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span>, dtypeBits<span class="tsd [...]
 					<aside class="tsd-sources">
 						<ul>
-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/ctypes.ts#L112">ctypes.ts:112</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/ctypes.ts#L112">ctypes.ts:112</a></li>
 						</ul>
 					</aside>
 					<div class="tsd-comment tsd-typography">
@@ -238,7 +238,7 @@
 					<div class="tsd-signature tsd-kind-icon">FTVMArray<wbr>Copy<wbr>From<wbr>Bytes<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span>handle<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a>, data<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a>, nbytes<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">num [...]
 					<aside class="tsd-sources">
 						<ul>
-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/ctypes.ts#L128">ctypes.ts:128</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/ctypes.ts#L128">ctypes.ts:128</a></li>
 						</ul>
 					</aside>
 					<div class="tsd-comment tsd-typography">
@@ -282,7 +282,7 @@
 					<div class="tsd-signature tsd-kind-icon">FTVMArray<wbr>Copy<wbr>From<wbr>To<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span>from<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a>, to<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a>, stream<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-sig [...]
 					<aside class="tsd-sources">
 						<ul>
-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/ctypes.ts#L144">ctypes.ts:144</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/ctypes.ts#L144">ctypes.ts:144</a></li>
 						</ul>
 					</aside>
 					<div class="tsd-comment tsd-typography">
@@ -326,7 +326,7 @@
 					<div class="tsd-signature tsd-kind-icon">FTVMArray<wbr>Copy<wbr>ToBytes<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span>handle<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a>, data<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a>, nbytes<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</sp [...]
 					<aside class="tsd-sources">
 						<ul>
-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/ctypes.ts#L136">ctypes.ts:136</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/ctypes.ts#L136">ctypes.ts:136</a></li>
 						</ul>
 					</aside>
 					<div class="tsd-comment tsd-typography">
@@ -370,7 +370,7 @@
 					<div class="tsd-signature tsd-kind-icon">FTVMArray<wbr>Free<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span>handle<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> =&gt; </span><span class="tsd-signature-type">number</span></div>
 					<aside class="tsd-sources">
 						<ul>
-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/ctypes.ts#L121">ctypes.ts:121</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/ctypes.ts#L121">ctypes.ts:121</a></li>
 						</ul>
 					</aside>
 					<div class="tsd-comment tsd-typography">
@@ -406,7 +406,7 @@
 					<div class="tsd-signature tsd-kind-icon">FTVMBackend<wbr>PackedCFunc<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span>argValues<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a>, argCodes<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a>, nargs<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number< [...]
 					<aside class="tsd-sources">
 						<ul>
-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/ctypes.ts#L160">ctypes.ts:160</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/ctypes.ts#L160">ctypes.ts:160</a></li>
 						</ul>
 					</aside>
 					<div class="tsd-comment tsd-typography">
@@ -458,7 +458,7 @@
 					<div class="tsd-signature tsd-kind-icon">FTVMCFunc<wbr>Set<wbr>Return<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span>ret<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a>, value<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a>, typeCode<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signa [...]
 					<aside class="tsd-sources">
 						<ul>
-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/ctypes.ts#L77">ctypes.ts:77</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/ctypes.ts#L77">ctypes.ts:77</a></li>
 						</ul>
 					</aside>
 					<div class="tsd-comment tsd-typography">
@@ -506,7 +506,7 @@
 					<div class="tsd-signature tsd-kind-icon">FTVMCb<wbr>Arg<wbr>ToReturn<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span>value<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a>, code<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> =&gt; </span><span c [...]
 					<aside class="tsd-sources">
 						<ul>
-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/ctypes.ts#L83">ctypes.ts:83</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/ctypes.ts#L83">ctypes.ts:83</a></li>
 						</ul>
 					</aside>
 					<div class="tsd-comment tsd-typography">
@@ -545,7 +545,7 @@
 					<div class="tsd-signature tsd-kind-icon">FTVMFunc<wbr>Call<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span>func<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a>, argValues<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a>, typeCode<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-t [...]
 					<aside class="tsd-sources">
 						<ul>
-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/ctypes.ts#L67">ctypes.ts:67</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/ctypes.ts#L67">ctypes.ts:67</a></li>
 						</ul>
 					</aside>
 					<div class="tsd-comment tsd-typography">
@@ -601,7 +601,7 @@
 					<div class="tsd-signature tsd-kind-icon">FTVMFunc<wbr>Free<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span>func<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> =&gt; </span><span class="tsd-signature-type">number</span></div>
 					<aside class="tsd-sources">
 						<ul>
-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/ctypes.ts#L57">ctypes.ts:57</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/ctypes.ts#L57">ctypes.ts:57</a></li>
 						</ul>
 					</aside>
 					<div class="tsd-comment tsd-typography">
@@ -637,7 +637,7 @@
 					<div class="tsd-signature tsd-kind-icon">FTVMFunc<wbr>Get<wbr>Global<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span>name<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a>, out<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> =&gt; </span><span cla [...]
 					<aside class="tsd-sources">
 						<ul>
-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/ctypes.ts#L100">ctypes.ts:100</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/ctypes.ts#L100">ctypes.ts:100</a></li>
 						</ul>
 					</aside>
 					<div class="tsd-comment tsd-typography">
@@ -676,7 +676,7 @@
 					<div class="tsd-signature tsd-kind-icon">FTVMFunc<wbr>List<wbr>Global<wbr>Names<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span>outSize<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a>, outArray<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> =&g [...]
 					<aside class="tsd-sources">
 						<ul>
-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/ctypes.ts#L88">ctypes.ts:88</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/ctypes.ts#L88">ctypes.ts:88</a></li>
 						</ul>
 					</aside>
 					<div class="tsd-comment tsd-typography">
@@ -715,7 +715,7 @@
 					<div class="tsd-signature tsd-kind-icon">FTVMFunc<wbr>Register<wbr>Global<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span>name<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a>, f<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a>, override<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</spa [...]
 					<aside class="tsd-sources">
 						<ul>
-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/ctypes.ts#L94">ctypes.ts:94</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/ctypes.ts#L94">ctypes.ts:94</a></li>
 						</ul>
 					</aside>
 					<div class="tsd-comment tsd-typography">
@@ -758,7 +758,7 @@
 					<div class="tsd-signature tsd-kind-icon">FTVMGet<wbr>Last<wbr>Error<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> =&gt; </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a></div>
 					<aside class="tsd-sources">
 						<ul>
-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/ctypes.ts#L34">ctypes.ts:34</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/ctypes.ts#L34">ctypes.ts:34</a></li>
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 					<div class="tsd-comment tsd-typography">
@@ -788,7 +788,7 @@
 					<div class="tsd-signature tsd-kind-icon">FTVMMod<wbr>Free<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span>mod<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> =&gt; </span><span class="tsd-signature-type">number</span></div>
 					<aside class="tsd-sources">
 						<ul>
-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/ctypes.ts#L52">ctypes.ts:52</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/ctypes.ts#L52">ctypes.ts:52</a></li>
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 					<div class="tsd-comment tsd-typography">
@@ -824,7 +824,7 @@
 					<div class="tsd-signature tsd-kind-icon">FTVMMod<wbr>Get<wbr>Function<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span>mod<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a>, funcName<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a>, queryImports<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">numbe [...]
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-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/ctypes.ts#L42">ctypes.ts:42</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/ctypes.ts#L42">ctypes.ts:42</a></li>
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 					<div class="tsd-comment tsd-typography">
@@ -872,7 +872,7 @@
 					<div class="tsd-signature tsd-kind-icon">FTVMMod<wbr>Import<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span>mod<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a>, dep<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> =&gt; </span><span class="tsd-si [...]
 					<aside class="tsd-sources">
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-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/ctypes.ts#L48">ctypes.ts:48</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/ctypes.ts#L48">ctypes.ts:48</a></li>
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 					<div class="tsd-comment tsd-typography">
@@ -912,7 +912,7 @@
 					<div class="tsd-signature tsd-kind-icon">FTVMSynchronize<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span>deviceType<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span>, deviceId<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span>, stream<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a><span class="tsd-signatur [...]
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 						<ul>
-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/ctypes.ts#L150">ctypes.ts:150</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/ctypes.ts#L150">ctypes.ts:150</a></li>
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 					<div class="tsd-comment tsd-typography">
@@ -954,7 +954,7 @@
 					<div class="tsd-signature tsd-kind-icon">FTVMWasm<wbr>Alloc<wbr>Space<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span>size<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> =&gt; </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a></div>
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-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/ctypes.ts#L167">ctypes.ts:167</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/ctypes.ts#L167">ctypes.ts:167</a></li>
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 					<div class="tsd-comment tsd-typography">
@@ -990,7 +990,7 @@
 					<div class="tsd-signature tsd-kind-icon">FTVMWasm<wbr>Free<wbr>Space<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span>ptr<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> =&gt; </span><span class="tsd-signature-type">void</span></div>
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-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/ctypes.ts#L170">ctypes.ts:170</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/ctypes.ts#L170">ctypes.ts:170</a></li>
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 					<div class="tsd-comment tsd-typography">
@@ -1026,7 +1026,7 @@
 					<div class="tsd-signature tsd-kind-icon">FTVMWasm<wbr>Func<wbr>Create<wbr>FromCFunc<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span>resource<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a>, out<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> =&g [...]
 					<aside class="tsd-sources">
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-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/ctypes.ts#L187">ctypes.ts:187</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/ctypes.ts#L187">ctypes.ts:187</a></li>
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 					<div class="tsd-comment tsd-typography">
@@ -1066,7 +1066,7 @@
 					<div class="tsd-signature tsd-kind-icon">FTVMWasm<wbr>PackedCFunc<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span>args<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a>, typeCodes<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a>, nargs<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span>, [...]
 					<aside class="tsd-sources">
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-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/ctypes.ts#L179">ctypes.ts:179</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/ctypes.ts#L179">ctypes.ts:179</a></li>
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 					<div class="tsd-comment tsd-typography">
@@ -1118,7 +1118,7 @@
 					<div class="tsd-signature tsd-kind-icon">FTVMWasm<wbr>PackedCFunc<wbr>Finalizer<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span>resourceHandle<span class="tsd-signature-symbol">: </span><a href="index.html#pointer" class="tsd-signature-type">Pointer</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> =&gt; </span><span class="tsd-signature-type">void</span></div>
 					<aside class="tsd-sources">
 						<ul>
-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/ctypes.ts#L193">ctypes.ts:193</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/ctypes.ts#L193">ctypes.ts:193</a></li>
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 					<div class="tsd-comment tsd-typography">
@@ -1154,7 +1154,7 @@
 					<div class="tsd-signature tsd-kind-icon">GPUPointer<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div>
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-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/webgpu.ts#L25">webgpu.ts:25</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/webgpu.ts#L25">webgpu.ts:25</a></li>
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 					<div class="tsd-comment tsd-typography">
@@ -1169,7 +1169,7 @@
 					<div class="tsd-signature tsd-kind-icon">Packed<wbr>Func<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">...</span>args<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">any</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> =&gt; </span><span class="tsd-signature-type">any</span><span class="tsd-signature-symbol"> &amp; </span><a href="interfaces/disp [...]
 					<aside class="tsd-sources">
 						<ul>
-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L36">runtime.ts:36</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L36">runtime.ts:36</a></li>
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 					<div class="tsd-comment tsd-typography">
@@ -1184,7 +1184,7 @@
 					<div class="tsd-signature tsd-kind-icon">Pointer<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div>
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-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/ctypes.ts#L25">ctypes.ts:25</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/ctypes.ts#L25">ctypes.ts:25</a></li>
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 					<div class="tsd-comment tsd-typography">
@@ -1199,7 +1199,7 @@
 					<div class="tsd-signature tsd-kind-icon">Ptr<wbr>Offset<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div>
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-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/ctypes.ts#L28">ctypes.ts:28</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/ctypes.ts#L28">ctypes.ts:28</a></li>
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@@ -1217,7 +1217,7 @@
 					<div class="tsd-signature tsd-kind-icon">RPC_<wbr>MAGIC<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">1045105</span><span class="tsd-signature-symbol"> = 1045105</span></div>
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-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/rpc_server.ts#L36">rpc_server.ts:36</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/rpc_server.ts#L36">rpc_server.ts:36</a></li>
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@@ -1239,7 +1239,7 @@
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-									<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/support.ts#L25">support.ts:25</a></li>
+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/support.ts#L25">support.ts:25</a></li>
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@@ -1271,7 +1271,7 @@
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-									<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/support.ts#L39">support.ts:39</a></li>
+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/support.ts#L39">support.ts:39</a></li>
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@@ -1300,7 +1300,7 @@
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-									<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/support.ts#L52">support.ts:52</a></li>
+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/support.ts#L52">support.ts:52</a></li>
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 							<div class="tsd-comment tsd-typography">
@@ -1337,7 +1337,7 @@
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-									<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/compact.ts#L38">compact.ts:38</a></li>
+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/compact.ts#L38">compact.ts:38</a></li>
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 							<div class="tsd-comment tsd-typography">
@@ -1368,7 +1368,7 @@
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-									<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/webgpu.ts#L30">webgpu.ts:30</a></li>
+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/webgpu.ts#L30">webgpu.ts:30</a></li>
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 							<div class="tsd-comment tsd-typography">
@@ -1390,7 +1390,7 @@
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-									<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/environment.ts#L32">environment.ts:32</a></li>
+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/environment.ts#L32">environment.ts:32</a></li>
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 							<div class="tsd-comment tsd-typography">
@@ -1421,7 +1421,7 @@
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-									<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/compact.ts#L24">compact.ts:24</a></li>
+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/compact.ts#L24">compact.ts:24</a></li>
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 							<div class="tsd-comment tsd-typography">
@@ -1443,7 +1443,7 @@
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-									<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L1356">runtime.ts:1356</a></li>
+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L1356">runtime.ts:1356</a></li>
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 							<div class="tsd-comment tsd-typography">
@@ -1508,7 +1508,7 @@
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-									<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/support.ts#L62">support.ts:62</a></li>
+									<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/support.ts#L62">support.ts:62</a></li>
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 							<div class="tsd-comment tsd-typography">
@@ -1530,7 +1530,7 @@
 					<div class="tsd-signature tsd-kind-icon">DLData<wbr>Type<wbr>Code<wbr>ToStr<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">object</span></div>
 					<aside class="tsd-sources">
 						<ul>
-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L246">runtime.ts:246</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L246">runtime.ts:246</a></li>
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 					<section class="tsd-panel tsd-member tsd-kind-variable tsd-parent-kind-object-literal">
@@ -1539,7 +1539,7 @@
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-								<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L247">runtime.ts:247</a></li>
+								<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L247">runtime.ts:247</a></li>
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@@ -1549,7 +1549,7 @@
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 							<ul>
-								<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L248">runtime.ts:248</a></li>
+								<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L248">runtime.ts:248</a></li>
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@@ -1559,7 +1559,7 @@
 						<div class="tsd-signature tsd-kind-icon">2<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> = &quot;float&quot;</span></div>
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 							<ul>
-								<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L249">runtime.ts:249</a></li>
+								<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L249">runtime.ts:249</a></li>
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@@ -1569,7 +1569,7 @@
 						<div class="tsd-signature tsd-kind-icon">3<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> = &quot;handle&quot;</span></div>
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 							<ul>
-								<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L250">runtime.ts:250</a></li>
+								<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L250">runtime.ts:250</a></li>
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@@ -1580,7 +1580,7 @@
 					<div class="tsd-signature tsd-kind-icon">Device<wbr>Enum<wbr>ToStr<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">object</span></div>
 					<aside class="tsd-sources">
 						<ul>
-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L175">runtime.ts:175</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L175">runtime.ts:175</a></li>
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 					<section class="tsd-panel tsd-member tsd-kind-variable tsd-parent-kind-object-literal">
@@ -1589,7 +1589,7 @@
 						<div class="tsd-signature tsd-kind-icon">1<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> = &quot;cpu&quot;</span></div>
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-								<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L176">runtime.ts:176</a></li>
+								<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L176">runtime.ts:176</a></li>
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@@ -1599,7 +1599,7 @@
 						<div class="tsd-signature tsd-kind-icon">15<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> = &quot;webgpu&quot;</span></div>
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-								<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L180">runtime.ts:180</a></li>
+								<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L180">runtime.ts:180</a></li>
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@@ -1609,7 +1609,7 @@
 						<div class="tsd-signature tsd-kind-icon">2<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> = &quot;cuda&quot;</span></div>
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-								<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L177">runtime.ts:177</a></li>
+								<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L177">runtime.ts:177</a></li>
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@@ -1619,7 +1619,7 @@
 						<div class="tsd-signature tsd-kind-icon">4<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> = &quot;opencl&quot;</span></div>
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-								<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L178">runtime.ts:178</a></li>
+								<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L178">runtime.ts:178</a></li>
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@@ -1629,7 +1629,7 @@
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-								<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L179">runtime.ts:179</a></li>
+								<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L179">runtime.ts:179</a></li>
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@@ -1640,7 +1640,7 @@
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 					<aside class="tsd-sources">
 						<ul>
-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L183">runtime.ts:183</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L183">runtime.ts:183</a></li>
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@@ -1649,7 +1649,7 @@
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-								<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L186">runtime.ts:186</a></li>
+								<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L186">runtime.ts:186</a></li>
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@@ -1659,7 +1659,7 @@
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 						<aside class="tsd-sources">
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-								<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L184">runtime.ts:184</a></li>
+								<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L184">runtime.ts:184</a></li>
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@@ -1669,7 +1669,7 @@
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 						<aside class="tsd-sources">
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-								<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L185">runtime.ts:185</a></li>
+								<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L185">runtime.ts:185</a></li>
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@@ -1679,7 +1679,7 @@
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-								<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L189">runtime.ts:189</a></li>
+								<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L189">runtime.ts:189</a></li>
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+								<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L187">runtime.ts:187</a></li>
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-								<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L188">runtime.ts:188</a></li>
+								<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L188">runtime.ts:188</a></li>
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-								<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/runtime.ts#L190">runtime.ts:190</a></li>
+								<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/runtime.ts#L190">runtime.ts:190</a></li>
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index e2bbedb74..854a4c80a 100644
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@@ -113,7 +113,7 @@
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-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/types.ts#L52">types.ts:52</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/types.ts#L52">types.ts:52</a></li>
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diff --git a/docs/reference/api/typedoc/interfaces/functioninfo.html b/docs/reference/api/typedoc/interfaces/functioninfo.html
index fb96410e1..21d5714db 100644
--- a/docs/reference/api/typedoc/interfaces/functioninfo.html
+++ b/docs/reference/api/typedoc/interfaces/functioninfo.html
@@ -95,7 +95,7 @@
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+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/webgpu.ts#L41">webgpu.ts:41</a></li>
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+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/webgpu.ts#L42">webgpu.ts:42</a></li>
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+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/webgpu.ts#L40">webgpu.ts:40</a></li>
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diff --git a/docs/reference/api/typedoc/interfaces/libraryprovider.html b/docs/reference/api/typedoc/interfaces/libraryprovider.html
index 01f4cb2c4..824d40559 100644
--- a/docs/reference/api/typedoc/interfaces/libraryprovider.html
+++ b/docs/reference/api/typedoc/interfaces/libraryprovider.html
@@ -112,7 +112,7 @@
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-							<li>Defined in <a href="https://github.com/apache/tvm/blob/fafabc96c/web/src/types.ts#L34">types.ts:34</a></li>
+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/types.ts#L34">types.ts:34</a></li>
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@@ -127,7 +127,7 @@
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+							<li>Defined in <a href="https://github.com/apache/tvm/blob/7d9b7bbd5/web/src/types.ts#L39">types.ts:39</a></li>
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diff --git a/docs/searchindex.js b/docs/searchindex.js
index de1245e62..539da03f1 100644
--- a/docs/searchindex.js
+++ b/docs/searchindex.js
@@ -1 +1 @@
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+Search.setIndex({docnames:["arch/benchmark","arch/convert_layout","arch/debugger","arch/device_target_interactions","arch/frontend/tensorflow","arch/hybrid_script","arch/index","arch/inferbound","arch/introduction_to_module_serialization","arch/microtvm_design","arch/microtvm_project_api","arch/model_library_format","arch/pass_infra","arch/relay_intro","arch/relay_op_strategy","arch/runtime","arch/runtimes/vulkan","arch/security","arch/virtual_machine","contribute/ci","contribute/code_gu [...]
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diff --git a/docs/topic/vta/tutorials/autotvm/sg_execution_times.html b/docs/topic/vta/tutorials/autotvm/sg_execution_times.html
index addb3741a..60a241b09 100644
--- a/docs/topic/vta/tutorials/autotvm/sg_execution_times.html
+++ b/docs/topic/vta/tutorials/autotvm/sg_execution_times.html
@@ -300,10 +300,10 @@
             
   <div class="section" id="computation-times">
 <span id="sphx-glr-topic-vta-tutorials-autotvm-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>00:20.420</strong> total execution time for <strong>topic_vta_tutorials_autotvm</strong> files:</p>
+<p><strong>00:21.794</strong> total execution time for <strong>topic_vta_tutorials_autotvm</strong> files:</p>
 <ul class="simple">
-<li><p><strong>00:20.224</strong>: <a class="reference internal" href="tune_relay_vta.html#sphx-glr-topic-vta-tutorials-autotvm-tune-relay-vta-py"><span class="std std-ref">Auto-tuning a convolutional network on VTA</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_relay_vta.py</span></code>)</p></li>
-<li><p><strong>00:00.196</strong>: <a class="reference internal" href="tune_alu_vta.html#sphx-glr-topic-vta-tutorials-autotvm-tune-alu-vta-py"><span class="std std-ref">Auto-tuning a ALU fused op on VTA</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_alu_vta.py</span></code>)</p></li>
+<li><p><strong>00:21.586</strong>: <a class="reference internal" href="tune_relay_vta.html#sphx-glr-topic-vta-tutorials-autotvm-tune-relay-vta-py"><span class="std std-ref">Auto-tuning a convolutional network on VTA</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_relay_vta.py</span></code>)</p></li>
+<li><p><strong>00:00.208</strong>: <a class="reference internal" href="tune_alu_vta.html#sphx-glr-topic-vta-tutorials-autotvm-tune-alu-vta-py"><span class="std std-ref">Auto-tuning a ALU fused op on VTA</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_alu_vta.py</span></code>)</p></li>
 </ul>
 </div>
 
diff --git a/docs/topic/vta/tutorials/frontend/deploy_classification.html b/docs/topic/vta/tutorials/frontend/deploy_classification.html
index 4761798c3..28eaf8497 100644
--- a/docs/topic/vta/tutorials/frontend/deploy_classification.html
+++ b/docs/topic/vta/tutorials/frontend/deploy_classification.html
@@ -539,7 +539,7 @@ and dense layer which will both be executed in fp32 on the CPU.</p></li>
   DeprecationWarning,
 /workspace/vta/tutorials/frontend/deploy_classification.py:213: DeprecationWarning: legacy graph executor behavior of producing json / lib / params will be removed in the next release. Please see documents of tvm.contrib.graph_executor.GraphModule for the  new recommended usage.
   relay_prog, target=tvm.target.Target(target, host=env.target_host), params=params
-resnet18_v1 inference graph built in 21.20s!
+resnet18_v1 inference graph built in 22.93s!
 </pre></div>
 </div>
 </div>
diff --git a/docs/topic/vta/tutorials/frontend/deploy_detection.html b/docs/topic/vta/tutorials/frontend/deploy_detection.html
index 0339591d8..313a3611f 100644
--- a/docs/topic/vta/tutorials/frontend/deploy_detection.html
+++ b/docs/topic/vta/tutorials/frontend/deploy_detection.html
@@ -557,7 +557,7 @@ and dense layer which will both be executed in fp32 on the CPU.</p></li>
 <p class="sphx-glr-script-out">Out:</p>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/workspace/python/tvm/relay/build_module.py:439: DeprecationWarning: Please use input parameter mod (tvm.IRModule) instead of deprecated parameter mod (tvm.relay.function.Function)
   DeprecationWarning,
-yolov3-tiny inference graph built in 14.79s!
+yolov3-tiny inference graph built in 15.89s!
 </pre></div>
 </div>
 </div>
diff --git a/docs/topic/vta/tutorials/frontend/sg_execution_times.html b/docs/topic/vta/tutorials/frontend/sg_execution_times.html
index a219a22dd..cd832b5e8 100644
--- a/docs/topic/vta/tutorials/frontend/sg_execution_times.html
+++ b/docs/topic/vta/tutorials/frontend/sg_execution_times.html
@@ -300,10 +300,10 @@
             
   <div class="section" id="computation-times">
 <span id="sphx-glr-topic-vta-tutorials-frontend-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>01:27.783</strong> total execution time for <strong>topic_vta_tutorials_frontend</strong> files:</p>
+<p><strong>01:31.294</strong> total execution time for <strong>topic_vta_tutorials_frontend</strong> files:</p>
 <ul class="simple">
-<li><p><strong>00:46.643</strong>: <a class="reference internal" href="deploy_detection.html#sphx-glr-topic-vta-tutorials-frontend-deploy-detection-py"><span class="std std-ref">Deploy Pretrained Vision Detection Model from Darknet on VTA</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_detection.py</span></code>)</p></li>
-<li><p><strong>00:41.140</strong>: <a class="reference internal" href="deploy_classification.html#sphx-glr-topic-vta-tutorials-frontend-deploy-classification-py"><span class="std std-ref">Deploy Pretrained Vision Model from MxNet on VTA</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_classification.py</span></code>)</p></li>
+<li><p><strong>00:48.077</strong>: <a class="reference internal" href="deploy_detection.html#sphx-glr-topic-vta-tutorials-frontend-deploy-detection-py"><span class="std std-ref">Deploy Pretrained Vision Detection Model from Darknet on VTA</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_detection.py</span></code>)</p></li>
+<li><p><strong>00:43.217</strong>: <a class="reference internal" href="deploy_classification.html#sphx-glr-topic-vta-tutorials-frontend-deploy-classification-py"><span class="std std-ref">Deploy Pretrained Vision Model from MxNet on VTA</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_classification.py</span></code>)</p></li>
 </ul>
 </div>
 
diff --git a/docs/topic/vta/tutorials/optimize/sg_execution_times.html b/docs/topic/vta/tutorials/optimize/sg_execution_times.html
index 96a6b78d1..c182c9571 100644
--- a/docs/topic/vta/tutorials/optimize/sg_execution_times.html
+++ b/docs/topic/vta/tutorials/optimize/sg_execution_times.html
@@ -300,10 +300,10 @@
             
   <div class="section" id="computation-times">
 <span id="sphx-glr-topic-vta-tutorials-optimize-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>00:03.500</strong> total execution time for <strong>topic_vta_tutorials_optimize</strong> files:</p>
+<p><strong>00:03.518</strong> total execution time for <strong>topic_vta_tutorials_optimize</strong> files:</p>
 <ul class="simple">
-<li><p><strong>00:02.969</strong>: <a class="reference internal" href="convolution_opt.html#sphx-glr-topic-vta-tutorials-optimize-convolution-opt-py"><span class="std std-ref">2D Convolution Optimization</span></a> (<code class="docutils literal notranslate"><span class="pre">convolution_opt.py</span></code>)</p></li>
-<li><p><strong>00:00.531</strong>: <a class="reference internal" href="matrix_multiply_opt.html#sphx-glr-topic-vta-tutorials-optimize-matrix-multiply-opt-py"><span class="std std-ref">Matrix Multiply Blocking</span></a> (<code class="docutils literal notranslate"><span class="pre">matrix_multiply_opt.py</span></code>)</p></li>
+<li><p><strong>00:02.973</strong>: <a class="reference internal" href="convolution_opt.html#sphx-glr-topic-vta-tutorials-optimize-convolution-opt-py"><span class="std std-ref">2D Convolution Optimization</span></a> (<code class="docutils literal notranslate"><span class="pre">convolution_opt.py</span></code>)</p></li>
+<li><p><strong>00:00.546</strong>: <a class="reference internal" href="matrix_multiply_opt.html#sphx-glr-topic-vta-tutorials-optimize-matrix-multiply-opt-py"><span class="std std-ref">Matrix Multiply Blocking</span></a> (<code class="docutils literal notranslate"><span class="pre">matrix_multiply_opt.py</span></code>)</p></li>
 </ul>
 </div>
 
diff --git a/docs/topic/vta/tutorials/sg_execution_times.html b/docs/topic/vta/tutorials/sg_execution_times.html
index b252ac771..bbd8abdb2 100644
--- a/docs/topic/vta/tutorials/sg_execution_times.html
+++ b/docs/topic/vta/tutorials/sg_execution_times.html
@@ -300,10 +300,10 @@
             
   <div class="section" id="computation-times">
 <span id="sphx-glr-topic-vta-tutorials-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>00:00.927</strong> total execution time for <strong>topic_vta_tutorials</strong> files:</p>
+<p><strong>00:00.985</strong> total execution time for <strong>topic_vta_tutorials</strong> files:</p>
 <ul class="simple">
-<li><p><strong>00:00.471</strong>: <a class="reference internal" href="matrix_multiply.html#sphx-glr-topic-vta-tutorials-matrix-multiply-py"><span class="std std-ref">Simple Matrix Multiply</span></a> (<code class="docutils literal notranslate"><span class="pre">matrix_multiply.py</span></code>)</p></li>
-<li><p><strong>00:00.456</strong>: <a class="reference internal" href="vta_get_started.html#sphx-glr-topic-vta-tutorials-vta-get-started-py"><span class="std std-ref">Get Started with VTA</span></a> (<code class="docutils literal notranslate"><span class="pre">vta_get_started.py</span></code>)</p></li>
+<li><p><strong>00:00.500</strong>: <a class="reference internal" href="matrix_multiply.html#sphx-glr-topic-vta-tutorials-matrix-multiply-py"><span class="std std-ref">Simple Matrix Multiply</span></a> (<code class="docutils literal notranslate"><span class="pre">matrix_multiply.py</span></code>)</p></li>
+<li><p><strong>00:00.485</strong>: <a class="reference internal" href="vta_get_started.html#sphx-glr-topic-vta-tutorials-vta-get-started-py"><span class="std std-ref">Get Started with VTA</span></a> (<code class="docutils literal notranslate"><span class="pre">vta_get_started.py</span></code>)</p></li>
 </ul>
 </div>
 
diff --git a/docs/tutorial/auto_scheduler_matmul_x86.html b/docs/tutorial/auto_scheduler_matmul_x86.html
index 5fc16e201..7e53b0723 100644
--- a/docs/tutorial/auto_scheduler_matmul_x86.html
+++ b/docs/tutorial/auto_scheduler_matmul_x86.html
@@ -453,7 +453,7 @@ trials, we can load the best schedule from the log file and apply it.</p>
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>*E
 </pre></div>
 </div>
 </div>
@@ -544,7 +544,7 @@ operator fusion.</p>
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time of this operator: 93.274 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time of this operator: 94.156 ms
 </pre></div>
 </div>
 </div>
@@ -620,6 +620,7 @@ automatically optimize a matrix multiplication, without the need to specify a
 search template.  It ends a series of examples that starts from the Tensor
 Expression (TE) language that demonstrates how TVM can optimize computational
 operations.</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes  11.447 seconds)</p>
 <div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-tutorial-auto-scheduler-matmul-x86-py">
 <div class="sphx-glr-download docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/eac4389b114db015e95cb3cdf8b86b83/auto_scheduler_matmul_x86.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">auto_scheduler_matmul_x86.py</span></code></a></p>
diff --git a/docs/tutorial/autotvm_relay_x86.html b/docs/tutorial/autotvm_relay_x86.html
index 7b99cc59d..d81602a81 100644
--- a/docs/tutorial/autotvm_relay_x86.html
+++ b/docs/tutorial/autotvm_relay_x86.html
@@ -513,7 +513,7 @@ standard deviation.</p>
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>{&#39;mean&#39;: 492.5690862900001, &#39;median&#39;: 492.4631355000031, &#39;std&#39;: 1.3102262374601348}
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>{&#39;mean&#39;: 501.5567130400007, &#39;median&#39;: 501.14271980000353, &#39;std&#39;: 1.2693202754407098}
 </pre></div>
 </div>
 </div>
@@ -667,129 +667,129 @@ depending on the specifics of the model and the target platform.</p>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[Task  1/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/10) | 0.00 s
-[Task  1/25]  Current/Best:   10.28/  10.28 GFLOPS | Progress: (4/10) | 6.02 s
-[Task  1/25]  Current/Best:    6.49/  23.64 GFLOPS | Progress: (8/10) | 8.43 s
-[Task  1/25]  Current/Best:    4.69/  23.73 GFLOPS | Progress: (10/10) | 10.78 s Done.
+[Task  1/25]  Current/Best:   23.71/  23.71 GFLOPS | Progress: (4/10) | 5.94 s
+[Task  1/25]  Current/Best:   13.13/  23.71 GFLOPS | Progress: (8/10) | 11.27 s
+[Task  1/25]  Current/Best:    9.69/  23.71 GFLOPS | Progress: (10/10) | 13.13 s Done.
 
 [Task  2/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/10) | 0.00 s
-[Task  2/25]  Current/Best:    8.29/  14.64 GFLOPS | Progress: (4/10) | 2.48 s
-[Task  2/25]  Current/Best:   16.06/  18.47 GFLOPS | Progress: (8/10) | 5.16 s
-[Task  2/25]  Current/Best:   15.88/  18.47 GFLOPS | Progress: (10/10) | 5.78 s Done.
+[Task  2/25]  Current/Best:   18.80/  18.80 GFLOPS | Progress: (4/10) | 2.77 s
+[Task  2/25]  Current/Best:   14.78/  18.80 GFLOPS | Progress: (8/10) | 4.25 s
+[Task  2/25]  Current/Best:    9.32/  18.80 GFLOPS | Progress: (10/10) | 6.25 s Done.
 
 [Task  3/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/10) | 0.00 s
-[Task  3/25]  Current/Best:   10.82/  10.82 GFLOPS | Progress: (4/10) | 3.50 s
-[Task  3/25]  Current/Best:   20.96/  23.00 GFLOPS | Progress: (8/10) | 5.06 s
-[Task  3/25]  Current/Best:   16.66/  23.00 GFLOPS | Progress: (10/10) | 5.80 s Done.
+[Task  3/25]  Current/Best:   12.42/  24.03 GFLOPS | Progress: (4/10) | 2.50 s
+[Task  3/25]  Current/Best:   11.36/  24.03 GFLOPS | Progress: (8/10) | 4.23 s
+[Task  3/25]  Current/Best:    8.89/  24.03 GFLOPS | Progress: (10/10) | 5.58 s Done.
 
 [Task  4/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/10) | 0.00 s
-[Task  4/25]  Current/Best:    7.86/  13.29 GFLOPS | Progress: (4/10) | 3.30 s
-[Task  4/25]  Current/Best:   13.72/  16.33 GFLOPS | Progress: (8/10) | 4.98 s
-[Task  4/25]  Current/Best:   20.22/  20.22 GFLOPS | Progress: (10/10) | 6.12 s Done.
+[Task  4/25]  Current/Best:   11.54/  17.92 GFLOPS | Progress: (4/10) | 3.77 s
+[Task  4/25]  Current/Best:    5.74/  18.77 GFLOPS | Progress: (8/10) | 5.32 s
+[Task  4/25]  Current/Best:   20.80/  20.80 GFLOPS | Progress: (10/10) | 5.88 s Done.
 
 [Task  5/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/10) | 0.00 s
-[Task  5/25]  Current/Best:   12.54/  16.00 GFLOPS | Progress: (4/10) | 2.67 s
-[Task  5/25]  Current/Best:    5.44/  17.62 GFLOPS | Progress: (8/10) | 4.55 s
-[Task  5/25]  Current/Best:   17.35/  17.62 GFLOPS | Progress: (10/10) | 5.43 s Done.
+[Task  5/25]  Current/Best:    4.06/  20.42 GFLOPS | Progress: (4/10) | 3.41 s
+[Task  5/25]  Current/Best:   18.43/  20.42 GFLOPS | Progress: (8/10) | 5.02 s
+[Task  5/25]  Current/Best:   13.22/  20.42 GFLOPS | Progress: (10/10) | 6.22 s Done.
 
 [Task  6/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/10) | 0.00 s
-[Task  6/25]  Current/Best:   19.37/  19.37 GFLOPS | Progress: (4/10) | 2.92 s
-[Task  6/25]  Current/Best:   10.08/  19.37 GFLOPS | Progress: (8/10) | 4.92 s
-[Task  6/25]  Current/Best:    9.78/  19.37 GFLOPS | Progress: (10/10) | 6.24 s Done.
+[Task  6/25]  Current/Best:   19.51/  19.51 GFLOPS | Progress: (4/10) | 3.21 s
+[Task  6/25]  Current/Best:    8.57/  19.51 GFLOPS | Progress: (8/10) | 5.04 s
+[Task  6/25]  Current/Best:   10.05/  19.51 GFLOPS | Progress: (10/10) | 7.24 s Done.
 
 [Task  7/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/10) | 0.00 s
-[Task  7/25]  Current/Best:   15.97/  16.63 GFLOPS | Progress: (4/10) | 2.73 s
-[Task  7/25]  Current/Best:   17.82/  17.82 GFLOPS | Progress: (8/10) | 4.52 s
-[Task  7/25]  Current/Best:   14.28/  19.29 GFLOPS | Progress: (10/10) | 5.29 s Done.
+[Task  7/25]  Current/Best:    1.59/  17.84 GFLOPS | Progress: (4/10) | 5.29 s
+[Task  7/25]  Current/Best:   15.33/  17.84 GFLOPS | Progress: (8/10) | 8.43 s
+[Task  7/25]  Current/Best:   23.06/  23.06 GFLOPS | Progress: (10/10) | 9.15 s Done.
 
 [Task  8/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/10) | 0.00 s
-[Task  8/25]  Current/Best:   12.66/  12.73 GFLOPS | Progress: (4/10) | 3.66 s
-[Task  8/25]  Current/Best:   10.49/  15.92 GFLOPS | Progress: (8/10) | 11.18 s
-[Task  8/25]  Current/Best:   12.64/  15.92 GFLOPS | Progress: (10/10) | 12.10 s Done.
+[Task  8/25]  Current/Best:   10.99/  15.29 GFLOPS | Progress: (4/10) | 3.80 s
+[Task  8/25]  Current/Best:   10.80/  18.04 GFLOPS | Progress: (8/10) | 6.61 s
+[Task  8/25]  Current/Best:   13.55/  18.04 GFLOPS | Progress: (10/10) | 8.08 s Done.
 
 [Task  9/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/10) | 0.00 s
-[Task  9/25]  Current/Best:    6.93/  20.88 GFLOPS | Progress: (4/10) | 5.73 s
-[Task  9/25]  Current/Best:    9.11/  20.88 GFLOPS | Progress: (8/10) | 16.96 s
-[Task  9/25]  Current/Best:   15.31/  20.88 GFLOPS | Progress: (10/10) | 17.60 s
+[Task  9/25]  Current/Best:    4.76/  16.31 GFLOPS | Progress: (4/10) | 2.87 s
+[Task  9/25]  Current/Best:   16.50/  16.50 GFLOPS | Progress: (8/10) | 6.06 s
+[Task  9/25]  Current/Best:   12.54/  16.50 GFLOPS | Progress: (10/10) | 6.62 s Done.
+
 [Task 10/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/10) | 0.00 s
-[Task 10/25]  Current/Best:   12.11/  12.11 GFLOPS | Progress: (4/10) | 2.95 s
-[Task 10/25]  Current/Best:   14.79/  14.87 GFLOPS | Progress: (8/10) | 5.34 s
-[Task 10/25]  Current/Best:   13.11/  14.87 GFLOPS | Progress: (10/10) | 6.37 s Done.
+[Task 10/25]  Current/Best:    8.55/  16.74 GFLOPS | Progress: (4/10) | 3.41 s
+[Task 10/25]  Current/Best:   16.47/  16.74 GFLOPS | Progress: (8/10) | 5.79 s
+[Task 10/25]  Current/Best:   15.28/  16.74 GFLOPS | Progress: (10/10) | 6.57 s Done.
 
 [Task 11/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/10) | 0.00 s
-[Task 11/25]  Current/Best:   14.40/  20.34 GFLOPS | Progress: (4/10) | 3.58 s
-[Task 11/25]  Current/Best:   18.13/  20.67 GFLOPS | Progress: (8/10) | 5.20 s
-[Task 11/25]  Current/Best:   20.56/  20.67 GFLOPS | Progress: (10/10) | 6.31 s Done.
+[Task 11/25]  Current/Best:   14.63/  23.54 GFLOPS | Progress: (4/10) | 2.77 s
+[Task 11/25]  Current/Best:   21.30/  23.54 GFLOPS | Progress: (8/10) | 5.07 s
+[Task 11/25]  Current/Best:   11.13/  23.54 GFLOPS | Progress: (10/10) | 6.14 s Done.
 
 [Task 12/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/10) | 0.00 s
-[Task 12/25]  Current/Best:   15.05/  18.33 GFLOPS | Progress: (4/10) | 2.65 s
-[Task 12/25]  Current/Best:   10.02/  20.13 GFLOPS | Progress: (8/10) | 5.26 s
-[Task 12/25]  Current/Best:   17.95/  20.13 GFLOPS | Progress: (10/10) | 6.60 s Done.
+[Task 12/25]  Current/Best:    6.73/  18.65 GFLOPS | Progress: (4/10) | 3.75 s
+[Task 12/25]  Current/Best:   12.49/  18.99 GFLOPS | Progress: (8/10) | 5.60 s
+[Task 12/25]  Current/Best:   13.81/  18.99 GFLOPS | Progress: (10/10) | 7.43 s Done.
 
 [Task 13/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/10) | 0.00 s
-[Task 13/25]  Current/Best:   10.66/  18.87 GFLOPS | Progress: (4/10) | 3.95 s
-[Task 13/25]  Current/Best:   15.68/  18.87 GFLOPS | Progress: (8/10) | 7.03 s
-[Task 13/25]  Current/Best:   15.29/  18.87 GFLOPS | Progress: (10/10) | 7.98 s Done.
+[Task 13/25]  Current/Best:    8.87/  20.79 GFLOPS | Progress: (4/10) | 4.84 s
+[Task 13/25]  Current/Best:   22.39/  22.39 GFLOPS | Progress: (8/10) | 8.05 s
+[Task 13/25]  Current/Best:    3.10/  22.39 GFLOPS | Progress: (10/10) | 10.04 s Done.
 
 [Task 14/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/10) | 0.00 s
-[Task 14/25]  Current/Best:   14.55/  22.67 GFLOPS | Progress: (4/10) | 2.59 s
-[Task 14/25]  Current/Best:   12.84/  22.67 GFLOPS | Progress: (8/10) | 4.76 s
-[Task 14/25]  Current/Best:   11.20/  22.67 GFLOPS | Progress: (10/10) | 9.04 s Done.
-
+[Task 14/25]  Current/Best:   10.93/  11.60 GFLOPS | Progress: (4/10) | 3.86 s
+[Task 14/25]  Current/Best:    9.67/  11.60 GFLOPS | Progress: (8/10) | 7.08 s
+[Task 14/25]  Current/Best:   20.70/  20.70 GFLOPS | Progress: (10/10) | 7.85 s
 [Task 15/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/10) | 0.00 s
-[Task 15/25]  Current/Best:    3.13/  19.24 GFLOPS | Progress: (4/10) | 2.81 s
-[Task 15/25]  Current/Best:    7.10/  20.90 GFLOPS | Progress: (8/10) | 4.53 s
-[Task 15/25]  Current/Best:   14.32/  20.90 GFLOPS | Progress: (10/10) | 5.16 s
+[Task 15/25]  Current/Best:   12.40/  12.40 GFLOPS | Progress: (4/10) | 2.96 s
+[Task 15/25]  Current/Best:   18.90/  18.90 GFLOPS | Progress: (8/10) | 6.38 s
+[Task 15/25]  Current/Best:   13.83/  18.90 GFLOPS | Progress: (10/10) | 7.14 s
 [Task 16/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/10) | 0.00 s Done.
  Done.
 
-[Task 16/25]  Current/Best:    9.49/  15.85 GFLOPS | Progress: (4/10) | 2.90 s
-[Task 16/25]  Current/Best:   13.13/  20.91 GFLOPS | Progress: (8/10) | 4.53 s
-[Task 16/25]  Current/Best:    6.03/  20.91 GFLOPS | Progress: (10/10) | 5.70 s Done.
+[Task 16/25]  Current/Best:   16.05/  16.05 GFLOPS | Progress: (4/10) | 2.95 s
+[Task 16/25]  Current/Best:    7.42/  18.60 GFLOPS | Progress: (8/10) | 5.59 s
+[Task 16/25]  Current/Best:   16.47/  18.60 GFLOPS | Progress: (10/10) | 6.57 s Done.
 
 [Task 17/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/10) | 0.00 s
-[Task 17/25]  Current/Best:    3.10/  11.93 GFLOPS | Progress: (4/10) | 4.81 s
-[Task 17/25]  Current/Best:    3.11/  24.29 GFLOPS | Progress: (8/10) | 7.42 s
-[Task 17/25]  Current/Best:   12.75/  24.29 GFLOPS | Progress: (10/10) | 8.59 s Done.
+[Task 17/25]  Current/Best:   12.23/  17.59 GFLOPS | Progress: (4/10) | 3.52 s
+[Task 17/25]  Current/Best:   15.37/  22.76 GFLOPS | Progress: (8/10) | 5.79 s
+[Task 17/25]  Current/Best:   19.95/  22.76 GFLOPS | Progress: (10/10) | 6.77 s Done.
 
 [Task 18/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/10) | 0.00 s
-[Task 18/25]  Current/Best:   12.43/  16.49 GFLOPS | Progress: (4/10) | 3.55 s
-[Task 18/25]  Current/Best:   12.53/  18.23 GFLOPS | Progress: (8/10) | 6.10 s
-[Task 18/25]  Current/Best:   11.47/  18.23 GFLOPS | Progress: (10/10) | 6.86 s Done.
+[Task 18/25]  Current/Best:   15.50/  19.25 GFLOPS | Progress: (4/10) | 3.87 s
+[Task 18/25]  Current/Best:   17.22/  19.78 GFLOPS | Progress: (8/10) | 5.46 s
+[Task 18/25]  Current/Best:   12.43/  19.78 GFLOPS | Progress: (10/10) | 7.43 s Done.
 
 [Task 19/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/10) | 0.00 s
-[Task 19/25]  Current/Best:   14.29/  14.29 GFLOPS | Progress: (4/10) | 5.71 s
-[Task 19/25]  Current/Best:   15.07/  18.40 GFLOPS | Progress: (8/10) | 7.84 s
-[Task 19/25]  Current/Best:    6.19/  23.47 GFLOPS | Progress: (10/10) | 9.90 s Done.
+[Task 19/25]  Current/Best:   20.72/  20.72 GFLOPS | Progress: (4/10) | 4.51 s
+[Task 19/25]  Current/Best:   11.45/  20.72 GFLOPS | Progress: (8/10) | 6.72 s
+[Task 19/25]  Current/Best:   22.12/  22.12 GFLOPS | Progress: (10/10) | 9.08 s Done.
 
 [Task 20/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/10) | 0.00 s
-[Task 20/25]  Current/Best:   21.85/  21.85 GFLOPS | Progress: (4/10) | 3.56 s
-[Task 20/25]  Current/Best:    6.04/  21.85 GFLOPS | Progress: (8/10) | 7.38 s
-[Task 20/25]  Current/Best:    2.71/  21.85 GFLOPS | Progress: (10/10) | 9.16 s Done.
-
+[Task 20/25]  Current/Best:   17.28/  17.28 GFLOPS | Progress: (4/10) | 2.75 s
+[Task 20/25]  Current/Best:   19.15/  22.75 GFLOPS | Progress: (8/10) | 4.30 s
+[Task 20/25]  Current/Best:   14.72/  22.75 GFLOPS | Progress: (10/10) | 5.29 s
 [Task 21/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/10) | 0.00 s
-[Task 21/25]  Current/Best:   13.66/  16.36 GFLOPS | Progress: (4/10) | 2.66 s
-[Task 21/25]  Current/Best:   23.76/  23.76 GFLOPS | Progress: (8/10) | 3.94 s
-[Task 21/25]  Current/Best:    1.63/  23.76 GFLOPS | Progress: (10/10) | 5.16 s
+[Task 21/25]  Current/Best:    6.39/  11.46 GFLOPS | Progress: (4/10) | 4.17 s
+[Task 21/25]  Current/Best:   11.00/  18.59 GFLOPS | Progress: (8/10) | 6.58 s
+[Task 21/25]  Current/Best:   15.12/  18.59 GFLOPS | Progress: (10/10) | 8.98 s
 [Task 22/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/10) | 0.00 s
-[Task 22/25]  Current/Best:   18.89/  18.89 GFLOPS | Progress: (4/10) | 2.68 s
-[Task 22/25]  Current/Best:    6.02/  18.89 GFLOPS | Progress: (8/10) | 5.57 s
-[Task 22/25]  Current/Best:   18.12/  18.89 GFLOPS | Progress: (10/10) | 6.25 s Done.
+[Task 22/25]  Current/Best:    2.08/  19.35 GFLOPS | Progress: (4/10) | 3.17 s
+[Task 22/25]  Current/Best:   17.34/  20.89 GFLOPS | Progress: (8/10) | 5.20 s
+[Task 22/25]  Current/Best:   15.29/  20.89 GFLOPS | Progress: (10/10) | 6.21 s Done.
 
 [Task 23/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/10) | 0.00 s
-[Task 23/25]  Current/Best:    5.39/  19.67 GFLOPS | Progress: (4/10) | 10.13 s
-[Task 23/25]  Current/Best:   11.44/  20.19 GFLOPS | Progress: (8/10) | 12.75 s
-[Task 23/25]  Current/Best:   20.62/  20.62 GFLOPS | Progress: (10/10) | 13.57 s Done.
+[Task 23/25]  Current/Best:   21.31/  22.71 GFLOPS | Progress: (4/10) | 2.70 s
+[Task 23/25]  Current/Best:    6.14/  22.71 GFLOPS | Progress: (8/10) | 5.32 s
+[Task 23/25]  Current/Best:   13.06/  22.71 GFLOPS | Progress: (10/10) | 6.54 s Done.
 
 [Task 24/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/10) | 0.00 s
-[Task 24/25]  Current/Best:    4.41/   7.86 GFLOPS | Progress: (4/10) | 3.22 s
-[Task 24/25]  Current/Best:    4.36/   8.15 GFLOPS | Progress: (8/10) | 15.45 s
-[Task 24/25]  Current/Best:    4.05/   8.15 GFLOPS | Progress: (10/10) | 30.63 s
+[Task 24/25]  Current/Best:    2.91/   2.91 GFLOPS | Progress: (4/10) | 33.30 s
+[Task 24/25]  Current/Best:    2.40/   3.75 GFLOPS | Progress: (8/10) | 36.71 s
+[Task 24/25]  Current/Best:    3.12/   3.75 GFLOPS | Progress: (10/10) | 37.34 s
 [Task 25/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/10) | 0.00 s Done.
  Done.
+ Done.
 
-[Task 25/25]  Current/Best:    1.55/   1.55 GFLOPS | Progress: (4/10) | 34.45 s
-[Task 25/25]  Current/Best:    6.07/   8.23 GFLOPS | Progress: (8/10) | 51.29 s
-[Task 25/25]  Current/Best:    0.00/   8.23 GFLOPS | Progress: (10/10) | 62.50 s
+[Task 25/25]  Current/Best:    2.94/   8.46 GFLOPS | Progress: (4/10) | 14.90 s
+[Task 25/25]  Current/Best:    3.96/   8.46 GFLOPS | Progress: (8/10) | 19.64 s
+[Task 25/25]  Current/Best:    2.94/   8.46 GFLOPS | Progress: (10/10) | 20.16 s
 </pre></div>
 </div>
 <p>The output from this tuning process will look something like this:</p>
@@ -855,8 +855,8 @@ model using optimized operators to speed up our computations.</p>
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>class=&#39;n02123045 tabby, tabby cat&#39; with probability=0.621104
-class=&#39;n02123159 tiger cat&#39; with probability=0.356378
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>class=&#39;n02123045 tabby, tabby cat&#39; with probability=0.621103
+class=&#39;n02123159 tiger cat&#39; with probability=0.356379
 class=&#39;n02124075 Egyptian cat&#39; with probability=0.019712
 class=&#39;n02129604 tiger, Panthera tigris&#39; with probability=0.001215
 class=&#39;n04040759 radiator&#39; with probability=0.000262
@@ -894,8 +894,8 @@ improvement in comparing the optimized model to the unoptimized model.</p>
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>optimized: {&#39;mean&#39;: 431.6472741200005, &#39;median&#39;: 431.64223475000085, &#39;std&#39;: 0.9637452451115474}
-unoptimized: {&#39;mean&#39;: 492.5690862900001, &#39;median&#39;: 492.4631355000031, &#39;std&#39;: 1.3102262374601348}
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>optimized: {&#39;mean&#39;: 433.99948903000114, &#39;median&#39;: 433.8421493999988, &#39;std&#39;: 0.8912803859693517}
+unoptimized: {&#39;mean&#39;: 501.5567130400007, &#39;median&#39;: 501.14271980000353, &#39;std&#39;: 1.2693202754407098}
 </pre></div>
 </div>
 </div>
@@ -909,7 +909,7 @@ models.</p>
 <p>Here we presented a simple example using ResNet-50 v2 locally. However, TVM
 supports many more features including cross-compilation, remote execution and
 profiling/benchmarking.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 7 minutes  48.916 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 7 minutes  3.813 seconds)</p>
 <div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-tutorial-autotvm-relay-x86-py">
 <div class="sphx-glr-download docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/57a45d9bef1af358191e7d50043e652c/autotvm_relay_x86.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">autotvm_relay_x86.py</span></code></a></p>
diff --git a/docs/tutorial/cross_compilation_and_rpc.html b/docs/tutorial/cross_compilation_and_rpc.html
index 8d04b1c50..6ab62e7f9 100644
--- a/docs/tutorial/cross_compilation_and_rpc.html
+++ b/docs/tutorial/cross_compilation_and_rpc.html
@@ -496,7 +496,7 @@ device and returns the measured cost. Network overhead is excluded.</p>
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>1.344e-07 secs/op
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>1.324e-07 secs/op
 </pre></div>
 </div>
 </div>
diff --git a/docs/tutorial/intro_topi.html b/docs/tutorial/intro_topi.html
index 80c12e4a0..17c714455 100644
--- a/docs/tutorial/intro_topi.html
+++ b/docs/tutorial/intro_topi.html
@@ -458,7 +458,7 @@ we can schedule the following series of operations ending with <code class="code
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[stage(a, placeholder(a, 0x107bad80)), stage(b, placeholder(b, 0x229654d0)), stage(T_add, compute(T_add, body=[(a[ax0, ax1, ax2] + b[ax1, ax2])], axis=[iter_var(ax0, range(min=0, ext=100)), iter_var(ax1, range(min=0, ext=10)), iter_var(ax2, range(min=0, ext=10))], reduce_axis=[], tag=broadcast, attrs={})), stage(T_multiply, compute(T_multiply, body=[(a[ax0, ax1, ax2]*b[ax1, ax2])], axis=[ [...]
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[stage(a, placeholder(a, 0x240f16c0)), stage(b, placeholder(b, 0x29565310)), stage(T_add, compute(T_add, body=[(a[ax0, ax1, ax2] + b[ax1, ax2])], axis=[iter_var(ax0, range(min=0, ext=100)), iter_var(ax1, range(min=0, ext=10)), iter_var(ax2, range(min=0, ext=10))], reduce_axis=[], tag=broadcast, attrs={})), stage(T_multiply, compute(T_multiply, body=[(a[ax0, ax1, ax2]*b[ax1, ax2])], axis=[ [...]
 </pre></div>
 </div>
 <p>We can test the correctness by comparing with <code class="code docutils literal notranslate"><span class="pre">numpy</span></code> result as follows</p>
diff --git a/docs/tutorial/sg_execution_times.html b/docs/tutorial/sg_execution_times.html
index cd0a7f63d..18a0edf26 100644
--- a/docs/tutorial/sg_execution_times.html
+++ b/docs/tutorial/sg_execution_times.html
@@ -300,20 +300,20 @@
             
   <div class="section" id="computation-times">
 <span id="sphx-glr-tutorial-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>10:34.152</strong> total execution time for <strong>tutorial</strong> files:</p>
+<p><strong>10:06.284</strong> total execution time for <strong>tutorial</strong> files:</p>
 <ul class="simple">
-<li><p><strong>07:48.916</strong>: <a class="reference internal" href="autotvm_relay_x86.html#sphx-glr-tutorial-autotvm-relay-x86-py"><span class="std std-ref">Compiling and Optimizing a Model with the Python Interface (AutoTVM)</span></a> (<code class="docutils literal notranslate"><span class="pre">autotvm_relay_x86.py</span></code>)</p></li>
-<li><p><strong>01:00.960</strong>: <a class="reference internal" href="tensor_expr_get_started.html#sphx-glr-tutorial-tensor-expr-get-started-py"><span class="std std-ref">Working with Operators Using Tensor Expression</span></a> (<code class="docutils literal notranslate"><span class="pre">tensor_expr_get_started.py</span></code>)</p></li>
-<li><p><strong>00:49.020</strong>: <a class="reference internal" href="auto_scheduler_matmul_x86.html#sphx-glr-tutorial-auto-scheduler-matmul-x86-py"><span class="std std-ref">Optimizing Operators with Auto-scheduling</span></a> (<code class="docutils literal notranslate"><span class="pre">auto_scheduler_matmul_x86.py</span></code>)</p></li>
-<li><p><strong>00:26.868</strong>: <a class="reference internal" href="autotvm_matmul_x86.html#sphx-glr-tutorial-autotvm-matmul-x86-py"><span class="std std-ref">Optimizing Operators with Schedule Templates and AutoTVM</span></a> (<code class="docutils literal notranslate"><span class="pre">autotvm_matmul_x86.py</span></code>)</p></li>
-<li><p><strong>00:26.069</strong>: <a class="reference internal" href="relay_quick_start.html#sphx-glr-tutorial-relay-quick-start-py"><span class="std std-ref">Quick Start Tutorial for Compiling Deep Learning Models</span></a> (<code class="docutils literal notranslate"><span class="pre">relay_quick_start.py</span></code>)</p></li>
-<li><p><strong>00:01.264</strong>: <a class="reference internal" href="tensor_ir_blitz_course.html#sphx-glr-tutorial-tensor-ir-blitz-course-py"><span class="std std-ref">Blitz Course to TensorIR</span></a> (<code class="docutils literal notranslate"><span class="pre">tensor_ir_blitz_course.py</span></code>)</p></li>
-<li><p><strong>00:00.701</strong>: <a class="reference internal" href="intro_topi.html#sphx-glr-tutorial-intro-topi-py"><span class="std std-ref">Introduction to TOPI</span></a> (<code class="docutils literal notranslate"><span class="pre">intro_topi.py</span></code>)</p></li>
-<li><p><strong>00:00.216</strong>: <a class="reference internal" href="cross_compilation_and_rpc.html#sphx-glr-tutorial-cross-compilation-and-rpc-py"><span class="std std-ref">Cross Compilation and RPC</span></a> (<code class="docutils literal notranslate"><span class="pre">cross_compilation_and_rpc.py</span></code>)</p></li>
-<li><p><strong>00:00.042</strong>: <a class="reference internal" href="introduction.html#sphx-glr-tutorial-introduction-py"><span class="std std-ref">Introduction</span></a> (<code class="docutils literal notranslate"><span class="pre">introduction.py</span></code>)</p></li>
-<li><p><strong>00:00.035</strong>: <a class="reference internal" href="tvmc_command_line_driver.html#sphx-glr-tutorial-tvmc-command-line-driver-py"><span class="std std-ref">Compiling and Optimizing a Model with TVMC</span></a> (<code class="docutils literal notranslate"><span class="pre">tvmc_command_line_driver.py</span></code>)</p></li>
-<li><p><strong>00:00.032</strong>: <a class="reference internal" href="tvmc_python.html#sphx-glr-tutorial-tvmc-python-py"><span class="std std-ref">Getting Starting using TVMC Python: a high-level API for TVM</span></a> (<code class="docutils literal notranslate"><span class="pre">tvmc_python.py</span></code>)</p></li>
-<li><p><strong>00:00.030</strong>: <a class="reference internal" href="install.html#sphx-glr-tutorial-install-py"><span class="std std-ref">Installing TVM</span></a> (<code class="docutils literal notranslate"><span class="pre">install.py</span></code>)</p></li>
+<li><p><strong>07:03.813</strong>: <a class="reference internal" href="autotvm_relay_x86.html#sphx-glr-tutorial-autotvm-relay-x86-py"><span class="std std-ref">Compiling and Optimizing a Model with the Python Interface (AutoTVM)</span></a> (<code class="docutils literal notranslate"><span class="pre">autotvm_relay_x86.py</span></code>)</p></li>
+<li><p><strong>01:11.447</strong>: <a class="reference internal" href="auto_scheduler_matmul_x86.html#sphx-glr-tutorial-auto-scheduler-matmul-x86-py"><span class="std std-ref">Optimizing Operators with Auto-scheduling</span></a> (<code class="docutils literal notranslate"><span class="pre">auto_scheduler_matmul_x86.py</span></code>)</p></li>
+<li><p><strong>01:01.767</strong>: <a class="reference internal" href="tensor_expr_get_started.html#sphx-glr-tutorial-tensor-expr-get-started-py"><span class="std std-ref">Working with Operators Using Tensor Expression</span></a> (<code class="docutils literal notranslate"><span class="pre">tensor_expr_get_started.py</span></code>)</p></li>
+<li><p><strong>00:27.017</strong>: <a class="reference internal" href="relay_quick_start.html#sphx-glr-tutorial-relay-quick-start-py"><span class="std std-ref">Quick Start Tutorial for Compiling Deep Learning Models</span></a> (<code class="docutils literal notranslate"><span class="pre">relay_quick_start.py</span></code>)</p></li>
+<li><p><strong>00:19.980</strong>: <a class="reference internal" href="autotvm_matmul_x86.html#sphx-glr-tutorial-autotvm-matmul-x86-py"><span class="std std-ref">Optimizing Operators with Schedule Templates and AutoTVM</span></a> (<code class="docutils literal notranslate"><span class="pre">autotvm_matmul_x86.py</span></code>)</p></li>
+<li><p><strong>00:01.097</strong>: <a class="reference internal" href="tensor_ir_blitz_course.html#sphx-glr-tutorial-tensor-ir-blitz-course-py"><span class="std std-ref">Blitz Course to TensorIR</span></a> (<code class="docutils literal notranslate"><span class="pre">tensor_ir_blitz_course.py</span></code>)</p></li>
+<li><p><strong>00:00.727</strong>: <a class="reference internal" href="intro_topi.html#sphx-glr-tutorial-intro-topi-py"><span class="std std-ref">Introduction to TOPI</span></a> (<code class="docutils literal notranslate"><span class="pre">intro_topi.py</span></code>)</p></li>
+<li><p><strong>00:00.222</strong>: <a class="reference internal" href="cross_compilation_and_rpc.html#sphx-glr-tutorial-cross-compilation-and-rpc-py"><span class="std std-ref">Cross Compilation and RPC</span></a> (<code class="docutils literal notranslate"><span class="pre">cross_compilation_and_rpc.py</span></code>)</p></li>
+<li><p><strong>00:00.054</strong>: <a class="reference internal" href="introduction.html#sphx-glr-tutorial-introduction-py"><span class="std std-ref">Introduction</span></a> (<code class="docutils literal notranslate"><span class="pre">introduction.py</span></code>)</p></li>
+<li><p><strong>00:00.054</strong>: <a class="reference internal" href="tvmc_command_line_driver.html#sphx-glr-tutorial-tvmc-command-line-driver-py"><span class="std std-ref">Compiling and Optimizing a Model with TVMC</span></a> (<code class="docutils literal notranslate"><span class="pre">tvmc_command_line_driver.py</span></code>)</p></li>
+<li><p><strong>00:00.053</strong>: <a class="reference internal" href="install.html#sphx-glr-tutorial-install-py"><span class="std std-ref">Installing TVM</span></a> (<code class="docutils literal notranslate"><span class="pre">install.py</span></code>)</p></li>
+<li><p><strong>00:00.052</strong>: <a class="reference internal" href="tvmc_python.html#sphx-glr-tutorial-tvmc-python-py"><span class="std std-ref">Getting Starting using TVMC Python: a high-level API for TVM</span></a> (<code class="docutils literal notranslate"><span class="pre">tvmc_python.py</span></code>)</p></li>
 </ul>
 </div>
 
diff --git a/docs/tutorial/tensor_expr_get_started.html b/docs/tutorial/tensor_expr_get_started.html
index 571927a84..51b3cd480 100644
--- a/docs/tutorial/tensor_expr_get_started.html
+++ b/docs/tutorial/tensor_expr_get_started.html
@@ -507,7 +507,7 @@ helper function to run a profile of the TVM generated code.</p>
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Numpy running time: 0.000009
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Numpy running time: 0.000007
 naive: 0.000007
 </pre></div>
 </div>
@@ -558,7 +558,7 @@ compile and run this new schedule with the parallel operation applied:</p>
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>parallel: 0.000007
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>parallel: 0.000006
 </pre></div>
 </div>
 </div>
@@ -631,10 +631,10 @@ factor to be the number of threads on your CPU.</p>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Operator                  Timing             Performance
-   numpy    8.90588999936881e-06                     1.0
-   naive              6.8638e-06      0.7707034334004194
-parallel              7.0911e-06      0.7962258685546947
-  vector             2.45479e-05      2.7563668540415156
+   numpy    7.305290000658715e-06                    1.0
+   naive    6.7811999999999996e-06    0.9282588370055865
+parallel              6.1035e-06      0.8354904458891639
+  vector              2.4503e-05       3.354144735909262
 </pre></div>
 </div>
 <div class="admonition-code-specialization admonition">
@@ -952,7 +952,7 @@ matrix multiplication.</p>
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Numpy running time: 0.018910
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Numpy running time: 0.019036
 </pre></div>
 </div>
 <p>Now we write a basic matrix multiplication using TVM TE and verify that it
@@ -994,7 +994,7 @@ optimizations.</p>
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>none: 3.389160
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>none: 3.414679
 </pre></div>
 </div>
 <p>Let’s take a look at the intermediate representation of the operator and
@@ -1060,7 +1060,7 @@ schedule.</p>
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>blocking: 0.315400
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>blocking: 0.320231
 </pre></div>
 </div>
 <p>By reordering the computation to take advantage of caching, you should see a
@@ -1120,7 +1120,7 @@ already cache friendly from our previous optimizations.</p>
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>vectorization: 0.346792
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>vectorization: 0.341613
 @main = primfn(A_1: handle, B_1: handle, C_1: handle) -&gt; ()
   attr = {&quot;from_legacy_te_schedule&quot;: True, &quot;global_symbol&quot;: &quot;main&quot;, &quot;tir.noalias&quot;: True}
   buffers = {A: Buffer(A_2: Pointer(float32), float32, [1048576], []),
@@ -1175,7 +1175,7 @@ more cache friendly.</p>
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>loop permutation: 0.115190
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>loop permutation: 0.136718
 @main = primfn(A_1: handle, B_1: handle, C_1: handle) -&gt; ()
   attr = {&quot;from_legacy_te_schedule&quot;: True, &quot;global_symbol&quot;: &quot;main&quot;, &quot;tir.noalias&quot;: True}
   buffers = {A: Buffer(A_2: Pointer(float32), float32, [1048576], []),
@@ -1251,7 +1251,7 @@ optimized schedule.</p>
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>array packing: 0.110624
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>array packing: 0.111712
 @main = primfn(A_1: handle, B_1: handle, C_1: handle) -&gt; ()
   attr = {&quot;from_legacy_te_schedule&quot;: True, &quot;global_symbol&quot;: &quot;main&quot;, &quot;tir.noalias&quot;: True}
   buffers = {A: Buffer(A_2: Pointer(float32), float32, [1048576], []),
@@ -1325,7 +1325,7 @@ to `C</cite> when all the block results are ready.</p>
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>block caching: 0.110961
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>block caching: 0.112373
 @main = primfn(A_1: handle, B_1: handle, C_1: handle) -&gt; ()
   attr = {&quot;from_legacy_te_schedule&quot;: True, &quot;global_symbol&quot;: &quot;main&quot;, &quot;tir.noalias&quot;: True}
   buffers = {A: Buffer(A_2: Pointer(float32), float32, [1048576], []),
@@ -1392,7 +1392,7 @@ of thread-level parallelization.</p>
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>parallelization: 0.144058
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>parallelization: 0.147064
 @main = primfn(A_1: handle, B_1: handle, C_1: handle) -&gt; ()
   attr = {&quot;from_legacy_te_schedule&quot;: True, &quot;global_symbol&quot;: &quot;main&quot;, &quot;tir.noalias&quot;: True}
   buffers = {A: Buffer(A_2: Pointer(float32), float32, [1048576], []),
@@ -1454,13 +1454,13 @@ working, we can compare the results.</p>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>        Operator                  Timing             Performance
-            none            3.3891596221                     1.0
-        blocking            0.3153996863      0.0930613253631799
-   vectorization     0.34679244389999997     0.10232402204919445
-loop permutation     0.11519021680000001     0.03398784053984024
-   array packing     0.11062351150000001     0.03264039580155719
-   block caching     0.11096075920000001     0.03273990356678633
- parallelization            0.1440581698    0.042505572431769466
+            none      3.4146787949999995                     1.0
+        blocking            0.3202308548      0.0937806669455714
+   vectorization            0.3416128965     0.10004246870897854
+loop permutation            0.1367184568     0.04003845310434243
+   array packing            0.1117116551     0.03271512836392567
+   block caching            0.1123728008     0.03290874707294395
+ parallelization     0.14706416309999998     0.04306822747584374
 </pre></div>
 </div>
 <p>Note that the outputs on the web page reflect the running times on a
@@ -1492,7 +1492,7 @@ is</p>
 you can build generic templates of the matrix multiplication and other
 operations with tunable parameters that allows you to automatically optimize
 the computation for specific platforms.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes  0.960 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes  1.767 seconds)</p>
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