code: def test_load_delta_table_name_with_version(spark_session, df):
      df_spark = spark_session.createDataFrame(df)
      df_spark.write.format("delta").mode("overwrite").saveAsTable(
          "my_delta_table_versioned"
      )
  
      df2 = pd.DataFrame(
          [[4, 5, 6], [4, 5, 6]], columns=["a", "b", "c"]
      )
      assert not df2.equals(df)
      df2_spark = spark_session.createDataFrame(df2)
      df2_spark.write.format("delta").mode("overwrite").saveAsTable(
          "my_delta_table_versioned"
      )
  
      mlflow_df = mlflow.data.load_delta(
          table_name="my_delta_table_versioned", version=1
      )
  
      _check_spark_dataset(
          mlflow_df,
          df2,
          df2_spark,
          DeltaDatasetSource,
          "my_delta_table_versioned@v1",
      )
      pd.testing.assert_frame_equal(mlflow_df.df.toPandas(), df2)
  
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 24 for calling a global function with example usage: test_load_delta_table_name_with_version(spark_session, df) without return types