code: def __init__(self, inplanes, planes, stride=1, downsample=None):
      super(Bottleneck, self).__init__()
      self.conv1 = conv1x1(inplanes, planes)
      self.bn1 = nn.BatchNorm2d(planes)
      self.conv2 = conv3x3(planes, planes, stride)
      self.bn2 = nn.BatchNorm2d(planes)
      self.conv3 = conv1x1(planes, planes * self.expansion)
      self.bn3 = nn.BatchNorm2d(planes * self.expansion)
      self.relu = nn.LeakyReLU(inplace=True)
      self.downsample = downsample
      self.stride = stride
  
imports: import torch
  import torch.nn
  import torch.utils.model_zoo
  from ..tools.custom_modules import SequentialWithArgs, FakeReLU
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)