code: def __init__(
      self,
      block,
      block_nums,
      num_classes=400,
      feat_ext=False,
      use_affine=True,
  ):
      self.use_affine = use_affine
      self.in_planes = 64
      self.num_classes = num_classes
      self.feat_ext = feat_ext
  
      super(ResNet_IRCSNv2, self).__init__()
  
      self.conv1 = nn.Conv3d(
          3,
          64,
          kernel_size=(3, 7, 7),
          stride=(1, 2, 2),
          padding=(1, 3, 3),
          bias=False,
      )
      if use_affine:
          self.bn1 = Affine(64)
      else:
          self.bn1 = nn.BatchNorm3d(
              64, track_running_stats=True, eps=eps, momentum=bn_mmt
          )
  
      self.relu = nn.ReLU(inplace=True)
      self.maxpool = nn.MaxPool3d(
          kernel_size=(1, 3, 3), stride=(1, 2, 2), padding=(0, 1, 1)
      )
  
      self.layer1 = self._make_layer(
          block,
          in_planes=64,
          planes=64,
          blocks=block_nums[0],
          stride=1,
          expansion=4,
      )
  
      self.layer2 = self._make_layer(
          block,
          in_planes=256,
          planes=128,
          blocks=block_nums[1],
          stride=2,
          temporal_stride=2,
          expansion=4,
      )
  
      self.layer3 = self._make_layer(
          block,
          in_planes=512,
          planes=256,
          blocks=block_nums[2],
          stride=2,
          temporal_stride=2,
          expansion=4,
      )
  
      self.layer4 = self._make_layer(
          block,
          in_planes=1024,
          planes=512,
          blocks=block_nums[3],
          stride=2,
          temporal_stride=2,
          expansion=4,
      )
  
      self.avgpool = nn.AdaptiveAvgPool3d(output_size=(1, 1, 1))
  
      self.out_fc = nn.Linear(
          in_features=2048, out_features=num_classes
      )
  
file_path: gluon-cv/gluoncv/torch/model_zoo/action_recognition/ircsnv2.py
imports: import torch
  import torch.nn
label_desc: Use image node_id 1 to create a new ResNet_IRCSNv2 object from inherited base classes: nn with example: obj = ResNet_IRCSNv2(block, block_nums, num_classes, feat_ext, use_affine)