code: def get_net(
      batch_size,
      image_shape,
      num_classes,
      dtype,
      num_layers=11,
      batch_norm=False,
  ):
      """
      Parameters
      ----------
      batch_size : int
          The batch size used in the model
  
      image_shape : tuple, optional
          The input image shape
  
      num_classes : int, optional
          Number of claseses
  
      dtype : str, optional
          The data type
  
      num_layers : int
          Number of layers for the variant of vgg. Options are 11, 13, 16, 19.
  
      batch_norm : bool, default False
          Use batch normalization.
      """
      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 num_layers not in vgg_spec:
          raise ValueError(
              f"Invalid num_layers {num_layers}. Choices are 11,13,16,19."
          )
      layers, filters = vgg_spec[num_layers]
      data_shape = (batch_size,) + image_shape
      data = relay.var("data", shape=data_shape, dtype=dtype)
      feature = get_feature(data, layers, filters, batch_norm)
      classifier = get_classifier(feature, num_classes)
      symbol = relay.nn.softmax(data=classifier)
      args = relay.analysis.free_vars(symbol)
      return relay.Function(args, symbol)
  
imports: from tvm import relay
  from .init import create_workload
  from .None import layers
label_desc: Use image node_id 3 for calling a global function with example usage: get_net(batch_size, image_shape, num_classes, dtype, num_layers, batch_norm) and returns: relay