class_docstr: Bottleneck block for ResNet-SlowFast.
      
code: def __init__(
      self,
      inplanes,
      planes,
      strides=1,
      downsample=None,
      head_conv=1,
      norm_layer=BatchNorm3d,
      norm_kwargs=None,
      layer_name="",
  ):
      super(Bottleneck, self).__init__()
  
      if head_conv == 1:
          self.conv1 = nn.Conv3d(
              in_channels=inplanes,
              out_channels=planes,
              kernel_size=1,
              bias=False,
          )
          self.bn1 = norm_layer(
              num_features=planes,
              **({} if norm_kwargs is None else norm_kwargs),
          )
      elif head_conv == 3:
          self.conv1 = nn.Conv3d(
              in_channels=inplanes,
              out_channels=planes,
              kernel_size=(3, 1, 1),
              padding=(1, 0, 0),
              bias=False,
          )
          self.bn1 = norm_layer(
              num_features=planes,
              **({} if norm_kwargs is None else norm_kwargs),
          )
      else:
          raise ValueError("Unsupported head_conv!")
      self.conv2 = nn.Conv3d(
          in_channels=planes,
          out_channels=planes,
          kernel_size=(1, 3, 3),
          stride=(1, strides, strides),
          padding=(0, 1, 1),
          bias=False,
      )
      self.bn2 = norm_layer(
          num_features=planes,
          **({} if norm_kwargs is None else norm_kwargs),
      )
      self.conv3 = nn.Conv3d(
          in_channels=planes,
          out_channels=planes * self.expansion,
          kernel_size=1,
          stride=1,
          bias=False,
      )
      self.bn3 = norm_layer(
          num_features=planes * self.expansion,
          **({} if norm_kwargs is None else norm_kwargs),
      )
      self.relu = nn.ReLU(inplace=True)
      self.downsample = downsample
  
imports: import torch
  import torch.nn
  from torch.nn import BatchNorm3d
label_desc: Use image node_id 1 to create a new Bottleneck object from inherited base classes: nn with example: obj = Bottleneck(inplanes, planes, strides, downsample, head_conv, norm_layer, norm_kwargs, layer_name)