code: def __init__(
      self,
      num_input_features,
      growth_rate,
      bn_size,
      drop_rate,
      memory_efficient=False,
  ):
      super(_DenseLayer, self).__init__()
      self.add_module("norm1", nn.BatchNorm2d(num_input_features)),
      self.add_module("relu1", nn.ReLU(inplace=True)),
      self.add_module(
          "conv1",
          nn.Conv2d(
              num_input_features,
              bn_size * growth_rate,
              kernel_size=1,
              stride=1,
              bias=False,
          ),
      ),
      self.add_module("norm2", nn.BatchNorm2d(bn_size * growth_rate)),
      self.add_module("relu2", nn.ReLU(inplace=True)),
      self.add_module(
          "conv2",
          nn.Conv2d(
              bn_size * growth_rate,
              growth_rate,
              kernel_size=3,
              stride=1,
              padding=1,
              bias=False,
          ),
      ),
      self.drop_rate = float(drop_rate)
      self.memory_efficient = memory_efficient
  
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 _DenseLayer object from inherited base classes: nn with example: obj = _DenseLayer(num_input_features, growth_rate, bn_size, drop_rate, memory_efficient)