code: def get_symbol(
      num_classes,
      num_layers=11,
      batch_norm=False,
      dtype="float32",
      **kwargs,
  ):
      """
      Parameters
      ----------
      num_classes : int, default 1000
          Number of classification classes.
      num_layers : int
          Number of layers for the variant of densenet. Options are 11, 13, 16, 19.
      batch_norm : bool, default False
          Use batch normalization.
      dtype: str, float32 or float16
          Data precision.
      """
      vgg_spec = {
          11: ([1, 1, 2, 2, 2], [64, 128, 256, 512, 512]),
          13: ([2, 2, 2, 2, 2], [64, 128, 256, 512, 512]),
          16: ([2, 2, 3, 3, 3], [64, 128, 256, 512, 512]),
          19: ([2, 2, 4, 4, 4], [64, 128, 256, 512, 512]),
      }
      if not vgg_spec.has_key(num_layers):
          raise ValueError(
              "Invalide num_layers {}. Possible choices are 11,13,16,19.".format(
                  num_layers
              )
          )
      layers, filters = vgg_spec[num_layers]
      data = mx.sym.Variable(name="data")
      if dtype == "float16":
          data = mx.sym.Cast(data=data, dtype=np.float16)
      feature = get_feature(data, layers, filters, batch_norm)
      classifier = get_classifier(feature, num_classes)
      if dtype == "float16":
          classifier = mx.sym.Cast(data=classifier, dtype=np.float32)
      symbol = mx.sym.SoftmaxOutput(data=classifier, name="softmax")
      return symbol
  
imports: import mxnet
  import numpy
label_desc: Use image node_id 3 for calling a global function with example usage: get_symbol(num_classes, num_layers, batch_norm, dtype) and returns: symbol