class_docstr: Computes data to be passed as argument inputs in sequence processing tests, the `compute_params`
  params should return a lists of ArgData describing inputs of the operator, while `expand_params`
  should return corresponding unfolded/expanded ArgData to be used in the baseline pipeline.
class_docstr_tok: ["Computes","data","to","be","passed","as","argument","inputs","in","sequence","processing","tests",",","the","`","compute_params","`","params","should","return","a","lists","of","ArgData","describing","inputs","of","the","operator",",","while","`","expand_params","`","should","return","corresponding","unfolded\/expanded","ArgData","to","be","used","in","the","baseline","pipeline","."]
code: def unfold_input(self) -> ArgData:
      input_desc = self.input_data.desc
      num_expand = len(input_desc.expandable_prefix)
      unfolded_input = unfold_batches(self.input_data.data, num_expand)
      if input_desc.layout:
          unfolded_layout = input_desc.layout[num_expand:]
      else:
          unfolded_layout = input_desc.layout
      self.unfolded_input = ArgData(
          desc=ArgDesc(
              input_desc.name,
              "",
              input_desc.dest_device,
              unfolded_layout,
          ),
          data=unfolded_input,
      )
      return self.unfolded_input
  
imports: import os
  import random
  import numpy
  from typing import List, Union, Callable, Optional
  from dataclasses import dataclass, field
  from nvidia.dali import pipeline_def
  import nvidia.dali.fn
  from nvidia.dali import types
  import nvidia.dali.tensors
  from test_utils import get_dali_extra_path, check_batch
label_desc: Use image node_id 5 for calling the ParamsProviderBase obj's underlying member method code with example usage: obj.unfold_input() and returns: self