code: def get_workload(
      batch_size=1,
      num_classes=1000,
      num_layers=18,
      image_shape=(3, 16, 112, 112),
      layout="NCDHW",
      dtype="float32",
      **kwargs,
  ):
      """Get benchmark workload for resnet
  
      Parameters
      ----------
      batch_size : int
          The batch size used in the model
  
      num_classes : int, optional
          Number of classes
  
      num_layers : int, optional
          Number of layers
  
      image_shape : tuple, optional
          The input image shape
  
      layout: str
          The data layout for conv3d
  
      dtype : str, optional
          The data type
  
      kwargs : dict
          Extra arguments
  
      Returns
      -------
      mod : tvm.IRModule
          The relay module that contains a ResNet network.
  
      params : dict of str to NDArray
          The parameters.
      """
      net = get_net(
          batch_size=batch_size,
          num_classes=num_classes,
          num_layers=num_layers,
          image_shape=image_shape,
          dtype=dtype,
          layout=layout,
          **kwargs,
      )
      return create_workload(net)
  
imports: from tvm import relay
  from .init import create_workload
  from .None import layers
label_desc: Use image node_id 4 for calling a global function with example usage: get_workload(batch_size, num_classes, num_layers, image_shape, layout, dtype) and returns: create_workload