code: def _prep_args(
      inputs, kwargs, op_name, wrapper_name, disqualified_arguments
  ):
      def _prep_inputs(inputs, batch_size):
          inputs = list(inputs)
  
          for i, input in enumerate(inputs):
              if not isinstance(
                  input,
                  (_tensors.TensorListCPU, _tensors.TensorListGPU),
              ):
                  inputs[i] = _transform_data_to_tensorlist(
                      input, batch_size
                  )
  
          return inputs
  
      def _prep_kwargs(kwargs, batch_size):
          for key, value in kwargs.items():
              kwargs[key] = _Classification(
                  value, f"Argument {key}", arg_constant_len=batch_size
              ).data
  
          return kwargs
  
      _disqualify_arguments(
          wrapper_name, kwargs, disqualified_arguments
      )
  
      # Preprocess kwargs to get batch_size.
      batch_size = _choose_batch_size(
          inputs, kwargs.pop("batch_size", -1)
      )
      kwargs = _prep_kwargs(kwargs, batch_size)
      init_args, call_args = _ops._separate_kwargs(
          kwargs, _tensors.TensorListCPU
      )
  
      # Preprocess inputs, try to convert each input to TensorList.
      inputs = _prep_inputs(inputs, batch_size)
  
      init_args["max_batch_size"] = batch_size
      init_args["device"], init_args["device_id"] = _choose_device(
          op_name, wrapper_name, inputs, kwargs.get("device")
      )
  
      return inputs, init_args, call_args
  
imports: import sys
  from nvidia.dali import backend
  from nvidia.dali import internal
  from nvidia.dali import ops
  from nvidia.dali import tensors
  from nvidia.dali import types
  from nvidia.dali.external_source import _prep_data_for_feed_input
label_desc: Use image node_id 40 for calling a global function with example usage: _prep_args(inputs, kwargs, op_name, wrapper_name, disqualified_arguments) and returns: inputs,  init_args,  call_args, inputs, kwargs