code: def __init__(self, num_input_features, num_output_features):
      super(_Transition, self).__init__()
      self.add_module("norm", nn.BatchNorm2d(num_input_features))
      self.add_module("relu", nn.ReLU(inplace=True))
      self.add_module(
          "conv",
          nn.Conv2d(
              num_input_features,
              num_output_features,
              kernel_size=1,
              stride=1,
              bias=False,
          ),
      )
      self.add_module("pool", nn.AvgPool2d(kernel_size=2, stride=2))
  
imports: import re
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.utils.checkpoint
  from collections import OrderedDict
  from torch.hub import load_state_dict_from_url
  from torch import Tensor
  from torch.jit.annotations import List
  from ..tools.custom_modules import FakeReLU
label_desc: Use image node_id 1 to create a new _Transition object from inherited base classes: nn with example: obj = _Transition(num_input_features, num_output_features)