code: def get_feature(
      internel_layer, layers, filters, batch_norm=False, **kwargs
  ):
      for i, num in enumerate(layers):
          for j in range(num):
              internel_layer = mx.sym.Convolution(
                  data=internel_layer,
                  kernel=(3, 3),
                  pad=(1, 1),
                  num_filter=filters[i],
                  name="conv%s_%s" % (i + 1, j + 1),
              )
              if batch_norm:
                  internel_layer = mx.symbol.BatchNorm(
                      data=internel_layer,
                      name="bn%s_%s" % (i + 1, j + 1),
                  )
              internel_layer = mx.sym.Activation(
                  data=internel_layer,
                  act_type="relu",
                  name="relu%s_%s" % (i + 1, j + 1),
              )
          internel_layer = mx.sym.Pooling(
              data=internel_layer,
              pool_type="max",
              kernel=(2, 2),
              stride=(2, 2),
              name="pool%s" % (i + 1),
          )
      return internel_layer
  
imports: import mxnet
  import numpy
label_desc: Use image node_id 1 for calling a global function with example usage: get_feature(internel_layer, layers, filters, batch_norm) and returns: internel_layer