code: def test_datasetdict_from_parquet_split(
      split, parquet_path, tmp_path
  ):
      if split:
          path = {split: parquet_path}
      else:
          split = "train"
          path = {"train": parquet_path, "test": parquet_path}
      cache_dir = tmp_path / "cache"
      expected_features = {
          "col_1": "string",
          "col_2": "int64",
          "col_3": "float64",
      }
      dataset = DatasetDict.from_parquet(path, cache_dir=cache_dir)
      _check_parquet_datasetdict(
          dataset, expected_features, splits=list(path.keys())
      )
      assert all(dataset[split].split == split for split in path.keys())
  
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 13 for calling a global function with example usage: test_datasetdict_from_parquet_split(split, parquet_path, tmp_path) without return types