code: def _make_dense_layer(growth_rate, bn_size, dropout):
      new_features = nn.HybridSequential()
      new_features.add(nn.BatchNorm())
      new_features.add(nn.Activation("relu"))
      new_features.add(
          nn.Conv2D(
              bn_size * growth_rate, kernel_size=1, use_bias=False
          )
      )
      new_features.add(nn.BatchNorm())
      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 = nn.HybridConcatenate(axis=1)
      out.add(nn.Identity())
      out.add(new_features)
  
      return out
  
file_path: mxnet/python/mxnet/gluon/model_zoo/vision/densenet.py
imports: import os
  from ....device import cpu
  from ...block import HybridBlock
  from ...None import nn
  from ....None import base
  from ....util import use_np, wrap_ctx_to_device_func
label_desc: Use image node_id 2 for calling a global function with example usage: _make_dense_layer(growth_rate, bn_size, dropout) and returns: out