code: def _make_dense_layer(
      growth_rate, bn_size, dropout, norm_layer, norm_kwargs
  ):
      new_features = nn.HybridSequential(prefix="")
      new_features.add(
          norm_layer(**({} if norm_kwargs is None else norm_kwargs))
      )
      new_features.add(nn.Activation("relu"))
      new_features.add(
          nn.Conv2D(
              bn_size * growth_rate, kernel_size=1, use_bias=False
          )
      )
      new_features.add(
          norm_layer(**({} if norm_kwargs is None else norm_kwargs))
      )
      new_features.add(nn.Activation("relu"))
      new_features.add(
          nn.Conv2D(
              growth_rate, kernel_size=3, padding=1, use_bias=False
          )
      )
      if dropout:
          new_features.add(nn.Dropout(dropout))
  
      out = HybridConcurrent(axis=1, prefix="")
      out.add(Identity())
      out.add(new_features)
  
      return out
  
file_path: gluon-cv/gluoncv/model_zoo/densenet.py
imports: from mxnet.context import cpu
  from mxnet.gluon.block import HybridBlock
  from mxnet.gluon import nn
  from mxnet.gluon.nn import BatchNorm
  from mxnet.gluon.contrib.nn import HybridConcurrent, Identity
label_desc: Use image node_id 2 for calling a global function with example usage: _make_dense_layer(growth_rate, bn_size, dropout, norm_layer, norm_kwargs) and returns: out