code: def __init__(
      self,
      num_layers,
      num_input_features,
      bn_size,
      growth_rate,
      drop_rate,
  ):
      super(_DenseBlock, self).__init__()
      for i in range(num_layers):
          layer = _DenseLayer(
              num_input_features + i * growth_rate,
              growth_rate,
              bn_size,
              drop_rate,
          )
          self.add_module("denselayer%d" % (i + 1), layer)
  
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 _DenseBlock object from inherited base classes: nn with example: obj = _DenseBlock(num_layers, num_input_features, bn_size, growth_rate, drop_rate)