code: def __init__(self, in_planes, growth_rate):
      super(Bottleneck, self).__init__()
      self.bn1 = nn.BatchNorm2d(in_planes)
      self.conv1 = nn.Conv2d(
          in_planes, 4 * growth_rate, kernel_size=1, bias=False
      )
      self.bn2 = nn.BatchNorm2d(4 * growth_rate)
      self.conv2 = nn.Conv2d(
          4 * growth_rate,
          growth_rate,
          kernel_size=3,
          padding=1,
          bias=False,
      )
  
imports: import math
  import torch
  import torch.nn
  import torch.nn.functional
  from ..tools.custom_modules import FakeReLU
label_desc: Use image node_id 1 to create a new Bottleneck object from inherited base classes: nn with example: obj = Bottleneck(in_planes, growth_rate)