class_docstr: This helper test class takes in any supported type of and runs some tests.
  The user is required to pass in the data that needs to sparsified and the
  runner will run some tests that needs to be passed in order for the data
  type to be supported.
  TODO: Change the structure by creating a separate test case class for each
        member function
class_docstr_tok: ["This","helper","test","class","takes","in","any","supported","type","of","and","runs","some","tests",".","The","user","is","required","to","pass","in","the","data","that","needs","to","sparsified","and","the","runner","will","run","some","tests","that","needs","to","be","passed","in","order","for","the","data","type","to","be","supported",".","TODO",":","Change","the","structure","by","creating","a","separate","test","case","class","for","each","member","function"]
code: def _get_name_data_config(some_data, defaults=None):
      if isinstance(some_data, Tuple):
          # dealing with data_list
          name, data = some_data
          config = defaults
      else:
          # dealing with data_with_config
          name, data, config = (
              some_data["name"],
              some_data["data"],
              some_data["config"],
          )
      return name, data, config
  
imports: import logging
  import torch
  from torch.nn.utils.parametrize import is_parametrized
  from torch.testing._internal.common_utils import TestCase
  from typing import Tuple
  from torch import nn
  import itertools
  import math
  import copy
  from torch.ao.pruning._experimental.data_sparsifier import BaseDataSparsifier, DataNormSparsifier
  from torch.ao.pruning._experimental.data_sparsifier.quantization_utils import post_training_sparse_quantize
label_desc: Use image node_id 2 for calling the _BaseDataSparsiferTestCase obj's underlying member method code with example usage: obj._get_name_data_config(some_data, defaults) and returns: name,  data,  config