class_docstr: BottleNeck building block. Default: No use. Each block consists of two 1*n*n and one n*n*n convolutional layers
  with a ReLU activation function after each layer and residual connections.
code: def __init__(
      self, inplanes, planes, conv_builder, stride=1, downsample=None
  ):
      super(Bottleneck, self).__init__()
      midplanes = (inplanes * planes * 3 * 3 * 3) // (
          inplanes * 3 * 3 + 3 * planes
      )
  
      # 1x1x1
      self.conv1 = nn.Sequential(
          nn.Conv3d(inplanes, planes, kernel_size=1, bias=False),
          nn.BatchNorm3d(planes),
          nn.ReLU(inplace=True),
      )
      # Second kernel
      self.conv2 = nn.Sequential(
          conv_builder(planes, planes, midplanes, stride),
          nn.BatchNorm3d(planes),
          nn.ReLU(inplace=True),
      )
  
      # 1x1x1
      self.conv3 = nn.Sequential(
          nn.Conv3d(
              planes, planes * self.expansion, kernel_size=1, bias=False
          ),
          nn.BatchNorm3d(planes * self.expansion),
      )
      self.relu = nn.ReLU(inplace=True)
      self.downsample = downsample
      self.stride = stride
  
imports: import torch.nn
  from torch.hub import load_state_dict_from_url
label_desc: Use image node_id 1 to create a new Bottleneck object from inherited base classes: nn with example: obj = Bottleneck(inplanes, planes, conv_builder, stride, downsample)