code: def __init__(
      self,
      input_c: int,
      growth_rate: int,
      bn_size: int,
      drop_rate: float,
      memory_efficient: bool = False,
  ):
      super(_DenseLayer, self).__init__()
  
      self.add_module("norm1", nn.BatchNorm2d(input_c))
      self.add_module("relu1", nn.ReLU(inplace=True))
      self.add_module(
          "conv1",
          nn.Conv2d(
              in_channels=input_c,
              out_channels=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 = drop_rate
      self.memory_efficient = memory_efficient
  
imports: import re
  from typing import Any, List, Tuple
  from collections import OrderedDict
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.utils.checkpoint
  from torch import Tensor
label_desc: Use image node_id 1 to create a new _DenseLayer object from inherited base classes: nn with example: obj = _DenseLayer(input_c, growth_rate, bn_size, drop_rate, memory_efficient)