code: def test_datasetdict_from_csv_features(features, csv_path, tmp_path):
      cache_dir = tmp_path / "cache"
      # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
      default_expected_features = {
          "col_1": "int64",
          "col_2": "int64",
          "col_3": "float64",
      }
      expected_features = (
          features.copy() if features else default_expected_features
      )
      features = (
          Features(
              {
                  feature: Value(dtype)
                  for feature, dtype in features.items()
              }
          )
          if features is not None
          else None
      )
      dataset = DatasetDict.from_csv(
          {"train": csv_path}, features=features, cache_dir=cache_dir
      )
      _check_csv_datasetdict(dataset, expected_features)
  
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 4 for calling a global function with example usage: test_datasetdict_from_csv_features(features, csv_path, tmp_path) without return types