code: def test_load_delta_path_with_version(spark_session, tmp_path, df):
      path = str(tmp_path / "temp.delta")
  
      df_v0 = pd.DataFrame(
          [[4, 5, 6], [4, 5, 6]], columns=["a", "b", "c"]
      )
      assert not df_v0.equals(df)
      df_v0_spark = spark_session.createDataFrame(df_v0)
      df_v0_spark.write.format("delta").mode("overwrite").save(path)
  
      # write again to create a new version
      df_v1_spark = spark_session.createDataFrame(df)
      df_v1_spark.write.format("delta").mode("overwrite").save(path)
  
      mlflow_df = mlflow.data.load_delta(path=path, version=1)
      _check_spark_dataset(
          mlflow_df, df, df_v1_spark, DeltaDatasetSource
      )
  
imports: import json
  import os
  import pandas
  import pytest
  import mlflow.data
  from mlflow.data.code_dataset_source import CodeDatasetSource
  from mlflow.data.delta_dataset_source import DeltaDatasetSource
  from mlflow.data.spark_dataset import SparkDataset
  from mlflow.data.spark_dataset_source import SparkDatasetSource
  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 22 for calling a global function with example usage: test_load_delta_path_with_version(spark_session, tmp_path, df) without return types