code: def __init__(self, inplanes, planes, stride=1, downsample=None):
      super(Bottleneck, self).__init__()
      self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=True)
      self.bn1 = nn.BatchNorm2d(planes)
      self.conv2 = nn.Conv2d(
          planes,
          planes,
          kernel_size=3,
          stride=stride,
          padding=1,
          bias=True,
      )
      self.bn2 = nn.BatchNorm2d(planes)
      self.conv3 = nn.Conv2d(
          planes, planes * 4, kernel_size=1, bias=True
      )
      self.bn3 = nn.BatchNorm2d(planes * 4)
      self.relu = nn.ReLU(inplace=True)
      self.downsample = downsample
      self.stride = stride
  
imports: import torch.nn
  import torch.nn.functional
  import math
  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)