code: def _check_parquet_datasetdict(
      dataset_dict, expected_features, splits=("train",)
  ):
      assert isinstance(
          dataset_dict, (DatasetDict, IterableDatasetDict)
      )
      for split in splits:
          dataset = dataset_dict[split]
          assert len(list(dataset)) == 4
          assert dataset.features is not None
          assert set(dataset.features) == set(expected_features)
          for feature, expected_dtype in expected_features.items():
              assert dataset.features[feature].dtype == expected_dtype
  
file_path: datasets/tests/io/test_parquet.py
imports: import pyarrow.parquet
  import pytest
  from datasets import Audio, Dataset, DatasetDict, Features, IterableDatasetDict, NamedSplit, Sequence, Value, config
  from datasets.features.image import Image
  from datasets.info import DatasetInfo
  from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size
  from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
label_desc: Use image node_id 6 for calling a global function with example usage: _check_parquet_datasetdict(dataset_dict, expected_features, splits) without return types