code: def _make_dense_block(
      num_layers,
      bn_size,
      growth_rate,
      dropout,
      stage_index,
      norm_layer,
      norm_kwargs,
  ):
      out = nn.HybridSequential(prefix="stage%d_" % stage_index)
      with out.name_scope():
          for _ in range(num_layers):
              out.add(
                  _make_dense_layer(
                      growth_rate,
                      bn_size,
                      dropout,
                      norm_layer,
                      norm_kwargs,
                  )
              )
      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 1 for calling a global function with example usage: _make_dense_block(num_layers, bn_size, growth_rate, dropout, stage_index, norm_layer, norm_kwargs) and returns: out