code: def test_from_huggingface_dataset_constructs_expected_dataset_with_revision():
      new_revision = "c33cbf965006dba64f134f7bef69c53d5d0d285d"
      old_revision = "8ca2693371541a5ba2b23981de4222be3bef149f"
      ds_new = datasets.load_dataset(
          "rotten_tomatoes", split="train", revision=new_revision
      )
      ds_old = datasets.load_dataset(
          "rotten_tomatoes", split="train", revision=old_revision
      )
  
      mlflow_ds_new = mlflow.data.from_huggingface(
          ds_new, path="rotten_tomatoes", revision=new_revision
      )
      mlflow_ds_old = mlflow.data.from_huggingface(
          ds_old, path="rotten_tomatoes", revision=old_revision
      )
  
      reloaded_ds_new = mlflow_ds_new.source.load()
      reloaded_ds_old = mlflow_ds_old.source.load()
  
      # Newer versions of the rotten "rotten_tomatoes" has a `task_templates` field, while the older
      # one does not.
      assert reloaded_ds_new.task_templates
      assert not reloaded_ds_old.task_templates
  
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 2 for calling a global function with example usage: test_from_huggingface_dataset_constructs_expected_dataset_with_revision() without return types