code: def __init__(
      self,
      inplanes,
      planes,
      stride=1,
      dilation=1,
      downsample=None,
      previous_dilation=1,
  ):
      super().__init__()
      self.conv1 = nn.Conv2d(inplanes, planes, 1, bias=False)
      self.bn1 = nn.BatchNorm2d(planes)
      self.conv2 = nn.Conv2d(
          planes, planes, 3, stride, dilation, dilation, bias=False
      )
      self.bn2 = nn.BatchNorm2d(planes)
      self.conv3 = nn.Conv2d(planes, planes * 4, 1, bias=False)
      self.bn3 = nn.BatchNorm2d(planes * 4)
      self.relu = nn.ReLU(inplace=True)
      self.downsample = downsample
      self.dilation = dilation
      self.stride = stride
  
imports: import math
  import torch.utils.model_zoo
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.nn.modules.batchnorm import _BatchNorm
  from .None import settings
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, previous_dilation)