code: def _make_aux(in_channels, classes, norm_layer, norm_kwargs):
      out = nn.HybridSequential(prefix="")
      out.add(nn.AvgPool2D(pool_size=5, strides=3))
      out.add(
          _make_basic_conv(
              in_channels=in_channels,
              channels=128,
              kernel_size=1,
              norm_layer=norm_layer,
              norm_kwargs=norm_kwargs,
          )
      )
  
      out.add(nn.Flatten())
      out.add(nn.Dense(units=1024, in_units=2048))
      out.add(nn.Activation("relu"))
      out.add(nn.Dropout(0.7))
      out.add(nn.Dense(units=classes, in_units=1024))
      return out
  
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
label_desc: Use image node_id 12 for calling a global function with example usage: _make_aux(in_channels, classes, norm_layer, norm_kwargs) and returns: out