code: def DWise(channels, strides, kernel_size=3, **kwargs):
      out = nn.HybridSequential(**kwargs)
      with out.name_scope():
          out.add(
              nn.Conv2D(
                  channels,
                  kernel_size,
                  strides=strides,
                  padding=kernel_size // 2,
                  groups=channels,
                  use_bias=False,
              ),
              nn.BatchNorm(scale=True),
              Act()
              # nn.Activation('relu')
          )
      return out
  
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 4 for calling a global function with example usage: DWise(channels, strides, kernel_size) and returns: out