code: def test_from_huggingface_dataset_digest_is_consistent_for_large_ordered_datasets(
      tmp_path,
  ):
      assert (
          mlflow.data.huggingface_dataset._MAX_ROWS_FOR_DIGEST_COMPUTATION_AND_SCHEMA_INFERENCE
          < 200000
      )
  
      df = pd.DataFrame.from_dict(
          {
              "a": list(range(200000)),
              "b": list(range(200000)),
          }
      )
      data_dir = "data"
      os.makedirs(tmp_path / data_dir)
      df.to_csv(tmp_path / data_dir / "my_data.csv")
  
      ds = datasets.load_dataset(
          str(tmp_path),
          data_dir=data_dir,
          name="default",
          split="train",
      )
      mlflow_ds = mlflow.data.from_huggingface(
          ds, path=str(tmp_path), data_dir=data_dir
      )
      assert mlflow_ds.digest == "1dda4ce8"
  
imports: import json
  import os
  import datasets
  import pandas
  import pytest
  import mlflow.data
  import mlflow.data.huggingface_dataset
  from mlflow.data.code_dataset_source import CodeDatasetSource
  from mlflow.data.dataset_source_registry import get_dataset_source_from_json
  from mlflow.data.huggingface_dataset import HuggingFaceDataset
  from mlflow.data.huggingface_dataset_source import HuggingFaceDatasetSource
  from mlflow.exceptions import MlflowException
  from mlflow.models.evaluation.base import EvaluationDataset
  from mlflow.types.schema import Schema
  from mlflow.types.utils import _infer_schema
label_desc: Use image node_id 6 for calling a global function with example usage: test_from_huggingface_dataset_digest_is_consistent_for_large_ordered_datasets(tmp_path) without return types