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