code: def create_csn(
      *,
      # Input clip configs.
      input_channel: int = 3,
      # Model configs.
      model_depth: int = 50,
      model_num_class: int = 400,
      dropout_rate: float = 0,
      # Normalization configs.
      norm: Callable = nn.BatchNorm3d,
      # Activation configs.
      activation: Callable = nn.ReLU,
      # Stem configs.
      stem_dim_out: int = 64,
      stem_conv_kernel_size: Tuple[int] = (3, 7, 7),
      stem_conv_stride: Tuple[int] = (1, 2, 2),
      stem_pool: Callable = None,
      stem_pool_kernel_size: Tuple[int] = (1, 3, 3),
      stem_pool_stride: Tuple[int] = (1, 2, 2),
      # Stage configs.
      stage_conv_a_kernel_size: Tuple[int] = (1, 1, 1),
      stage_conv_b_kernel_size: Tuple[int] = (3, 3, 3),
      stage_conv_b_width_per_group: int = 1,
      stage_spatial_stride: Tuple[int] = (1, 2, 2, 2),
      stage_temporal_stride: Tuple[int] = (1, 2, 2, 2),
      bottleneck: Callable = create_bottleneck_block,
      bottleneck_ratio: int = 4,
      # Head configs.
      head_pool: Callable = nn.AvgPool3d,
      head_pool_kernel_size: Tuple[int] = (1, 7, 7),
      head_output_size: Tuple[int] = (1, 1, 1),
      head_activation: Callable = None,
      head_output_with_global_average: bool = True,
  ) -> nn.Module:
      """
      Build Channel-Separated Convolutional Networks (CSN):
      Video classification with channel-separated convolutional networks.
      Du Tran, Heng Wang, Lorenzo Torresani, Matt Feiszli. ICCV 2019.
  
      CSN follows the ResNet style architecture including three parts: Stem,
      Stages and Head. The three parts are assembled in the following order:
  
      ::
  
                                           Input
                                             ↓
                                           Stem
                                             ↓
                                           Stage 1
                                             ↓
                                             .
                                             .
                                             .
                                             ↓
                                           Stage N
                                             ↓
                                           Head
  
      CSN uses depthwise convolution. To further reduce the computational cost, it uses
      low resolution (112x112), short clips (4 frames), different striding and kernel
      size, etc.
  
      Args:
  
          input_channel (int): number of channels for the input video clip.
  
          model_depth (int): the depth of the resnet. Options include: 50, 101, 152.
              model_num_class (int): the number of classes for the video dataset.
              dropout_rate (float): dropout rate.
  
          norm (callable): a callable that constructs normalization layer.
  
          activation (callable): a callable that constructs activation layer.
  
          stem_dim_out (int): output channel size to stem.
          stem_conv_kernel_size (tuple): convolutional kernel size(s) of stem.
          stem_conv_stride (tuple): convolutional stride size(s) of stem.
          stem_pool (callable): a callable that constructs resnet head pooling layer.
          stem_pool_kernel_size (tuple): pooling kernel size(s).
          stem_pool_stride (tuple): pooling stride size(s).
  
          stage_conv_a_kernel_size (tuple): convolutional kernel size(s) for conv_a.
          stage_conv_b_kernel_size (tuple): convolutional kernel size(s) for conv_b.
          stage_conv_b_width_per_group(int): the width of each group for conv_b. Set
              it to 1 for depthwise convolution.
          stage_spatial_stride (tuple): the spatial stride for each stage.
          stage_temporal_stride (tuple): the temporal stride for each stage.
          bottleneck (callable): a callable that constructs bottleneck block layer.
              Examples include: create_bottleneck_block.
          bottleneck_ratio (int): the ratio between inner and outer dimensions for
              the bottleneck block.
  
          head_pool (callable): a callable that constructs resnet head pooling layer.
          head_pool_kernel_size (tuple): the pooling kernel size.
          head_output_size (tuple): the size of output tensor for head.
          head_activation (callable): a callable that constructs activation layer.
          head_output_with_global_average (bool): if True, perform global averaging on
              the head output.
  
      Returns:
          (nn.Module): the csn model.
      """
  
      torch._C._log_api_usage_once("PYTORCHVIDEO.model.create_csn")
  
      # Number of blocks for different stages given the model depth.
      _MODEL_STAGE_DEPTH = {
          50: (3, 4, 6, 3),
          101: (3, 4, 23, 3),
          152: (3, 8, 36, 3),
      }
  
      # Given a model depth, get the number of blocks for each stage.
      assert (
          model_depth in _MODEL_STAGE_DEPTH.keys()
      ), f"{model_depth} is not in {_MODEL_STAGE_DEPTH.keys()}"
      stage_depths = _MODEL_STAGE_DEPTH[model_depth]
  
      blocks = []
      # Create stem for CSN.
      stem = create_res_basic_stem(
          in_channels=input_channel,
          out_channels=stem_dim_out,
          conv_kernel_size=stem_conv_kernel_size,
          conv_stride=stem_conv_stride,
          conv_padding=[size // 2 for size in stem_conv_kernel_size],
          pool=stem_pool,
          pool_kernel_size=stem_pool_kernel_size,
          pool_stride=stem_pool_stride,
          pool_padding=[size // 2 for size in stem_pool_kernel_size],
          norm=norm,
          activation=activation,
      )
      blocks.append(stem)
  
      stage_dim_in = stem_dim_out
      stage_dim_out = stage_dim_in * 4
  
      # Create each stage for CSN.
      for idx in range(len(stage_depths)):
          stage_dim_inner = stage_dim_out // bottleneck_ratio
          depth = stage_depths[idx]
  
          stage_conv_b_stride = (
              stage_temporal_stride[idx],
              stage_spatial_stride[idx],
              stage_spatial_stride[idx],
          )
  
          stage = create_res_stage(
              depth=depth,
              dim_in=stage_dim_in,
              dim_inner=stage_dim_inner,
              dim_out=stage_dim_out,
              bottleneck=bottleneck,
              conv_a_kernel_size=stage_conv_a_kernel_size,
              conv_a_stride=(1, 1, 1),
              conv_a_padding=[
                  size // 2 for size in stage_conv_a_kernel_size
              ],
              conv_b_kernel_size=stage_conv_b_kernel_size,
              conv_b_stride=stage_conv_b_stride,
              conv_b_padding=[
                  size // 2 for size in stage_conv_b_kernel_size
              ],
              conv_b_num_groups=(
                  stage_dim_inner // stage_conv_b_width_per_group
              ),
              conv_b_dilation=(1, 1, 1),
              norm=norm,
              activation=activation,
          )
  
          blocks.append(stage)
          stage_dim_in = stage_dim_out
          stage_dim_out = stage_dim_out * 2
  
      # Create head for CSN.
      head = create_res_basic_head(
          in_features=stage_dim_in,
          out_features=model_num_class,
          pool=head_pool,
          output_size=head_output_size,
          pool_kernel_size=head_pool_kernel_size,
          dropout_rate=dropout_rate,
          activation=head_activation,
          output_with_global_average=head_output_with_global_average,
      )
      blocks.append(head)
      return Net(blocks=nn.ModuleList(blocks))
  
imports: from typing import Callable, Tuple
  import torch
  import torch.nn
  from pytorchvideo.models.head import create_res_basic_head
  from pytorchvideo.models.resnet import create_bottleneck_block, create_res_stage, Net
  from pytorchvideo.models.stem import create_res_basic_stem
label_desc: Use image node_id 1 for calling a global function with example usage: create_csn() and returns: Net