code: def __init__(
      self, num_input_features, growth_rate, bn_size, drop_rate
  ):
      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 = drop_rate
  
imports: from collections import OrderedDict
  import math
  import re
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.utils import model_zoo
  from torchvision.models.densenet import DenseNet
  from torchvision.models.squeezenet import squeezenet1_1
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)