code: def __init__(
      self, inplanes, planes, stride=1, downsample=None, dilation=1
  ):
      super(Bottleneck, self).__init__()
      self.conv1 = nn.Conv2d(
          inplanes, planes, kernel_size=1, bias=False
      )
      self.bn1 = nn.BatchNorm2d(planes)
      self.conv2 = nn.Conv2d(
          planes,
          planes,
          kernel_size=3,
          stride=stride,
          dilation=dilation,
          padding=dilation,
          bias=False,
      )
      self.bn2 = nn.BatchNorm2d(planes)
      self.conv3 = nn.Conv2d(
          planes, planes * 4, kernel_size=1, bias=False
      )
      self.bn3 = nn.BatchNorm2d(planes * 4)
      self.relu = nn.ReLU(inplace=True)
      self.downsample = downsample
      self.stride = stride
  
imports: from collections import OrderedDict
  import math
  import re
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.utils import model_zoo
  from torchvision.models.densenet import DenseNet
  from torchvision.models.squeezenet import squeezenet1_1
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, dilation)