code: def _create_dummy_iterable_dataset(
      self, multiple_columns=False
  ) -> IterableDataset:
      def gen():
          if multiple_columns:
              data = {
                  "col_1": [3, 2, 1, 0],
                  "col_2": ["a", "b", "c", "d"],
              }
              for v1, v2 in zip(data["col_1"], data["col_2"]):
                  yield {"col_1": v1, "col_2": v2}
          else:
              for x in range(30):
                  yield {"filename": "my_name-train" + "_" + f"{x:03d}"}
  
      return IterableDataset.from_generator(gen)
  
imports: import os
  import tempfile
  from unittest import TestCase
  import numpy
  import pandas
  import pytest
  from datasets import load_from_disk
  from datasets.arrow_dataset import Dataset
  from datasets.dataset_dict import DatasetDict, IterableDatasetDict
  from datasets.features import ClassLabel, Features, Sequence, Value
  from datasets.iterable_dataset import IterableDataset
  from datasets.splits import NamedSplit
  from .utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_tf, require_torch
label_desc: Use image node_id 3 for calling the DatasetDictTest obj's underlying member method code with example usage: obj._create_dummy_iterable_dataset(multiple_columns) and returns: IterableDataset