code: def __init__(
      self,
      inplanes,
      planes,
      stride=1,
      downsample=None,
      norm_layer=nn.BatchNorm2d,
  ):
      super(Bottleneck, self).__init__()
      # Both self.conv2 and self.downsample layers downsample the input when stride != 1
      self.conv1 = conv1x1(inplanes, planes)
      self.bn1 = norm_layer(planes)
      self.conv2 = conv3x3(planes, planes, stride)
      self.bn2 = norm_layer(planes)
      self.conv3 = conv1x1(planes, planes * self.expansion)
      self.bn3 = norm_layer(planes * self.expansion)
      self.relu = nn.ReLU(inplace=True)
      self.downsample = downsample
      self.stride = stride
  
imports: import torch
  import torch.nn
  import torch.utils.model_zoo
label_desc: Use image node_id 1 to create a new Bottleneck object from inherited base classes: nn with example: obj = Bottleneck(inplanes, planes, stride, downsample, norm_layer)