code: def test_from_spark_with_sql_and_version(spark_session, tmp_path, df):
      df_spark = spark_session.createDataFrame(df)
      path = str(tmp_path / "temp.parquet")
      df_spark.write.parquet(path)
      with pytest.raises(
          MlflowException,
          match="`version` may not be specified when `sql` is specified. `version` may only be"
          " specified when `table_name` or `path` is specified.",
      ):
          mlflow.data.from_spark(
              df_spark, sql="SELECT * FROM table", version=1
          )
  
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 11 for calling a global function with example usage: test_from_spark_with_sql_and_version(spark_session, tmp_path, df) without return types