code: def _make_dense_layer(growth_rate, bn_size, dropout):
      new_features = nn.HybridSequential(prefix="")
      new_features.add(nn.BatchNorm())
      # new_features.add(nn.Activation('relu'))
      new_features.add(Act())
      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(Act())
      new_features.add(
          nn.Conv2D(
              growth_rate, kernel_size=3, padding=1, use_bias=False
          )
      )
      if dropout:
          new_features.add(nn.Dropout(dropout))
  
      out = gluon.contrib.nn.HybridConcurrent(axis=1, prefix="")
      out.add(gluon.contrib.nn.Identity())
      out.add(new_features)
  
      return out
  
file_path: insightface/recognition/subcenter_arcface/symbol/fdensenet.py
imports: import sys
  import os
  import mxnet
  import mxnet.ndarray
  import mxnet.gluon
  import mxnet.gluon.nn
  import mxnet.autograd
  import symbol_utils
  from config import config
label_desc: Use image node_id 3 for calling a global function with example usage: _make_dense_layer(growth_rate, bn_size, dropout) and returns: out