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_output(self, batches):
      num_expand = len(self.input_data.desc.expandable_prefix)
      return unfold_batch(batches, num_expand)
  
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 3 for calling the ParamsProviderBase obj's underlying member method code with example usage: obj.unfold_output(batches) and returns: unfold_batch