code: def test_create_entityset_with_mixed_dataframe_types(pd_es, spark_es):
      df = pd.DataFrame(
          {"id": [0, 1, 2, 3], "values": [1, 12, -34, 27]}
      )
      spark_df = ps.from_pandas(df)
  
      err_msg = (
          "All dataframes must be of the same type. "
          "Cannot add dataframe of type {} to an entityset with existing dataframes "
          "of type {}"
      )
  
      # Test error is raised when trying to add Spark dataframe to entitset with existing pandas dataframes
      with pytest.raises(
          ValueError,
          match=err_msg.format(
              type(spark_df), type(pd_es.dataframes[0])
          ),
      ):
          pd_es.add_dataframe(
              dataframe_name="new_dataframe",
              dataframe=spark_df,
              index="id",
          )
  
      # Test error is raised when trying to add pandas dataframe to entitset with existing ps dataframes
      with pytest.raises(
          ValueError,
          match=err_msg.format(type(df), type(spark_es.dataframes[0])),
      ):
          spark_es.add_dataframe(
              dataframe_name="new_dataframe", dataframe=df, index="id"
          )
  
file_path: featuretools/featuretools/tests/entityset_tests/test_spark_es.py
imports: import pandas
  import pytest
  from woodwork.logical_types import Datetime, Double, Integer, NaturalLanguage
  from featuretools.entityset import EntitySet
  from featuretools.tests.testing_utils import get_df_tags
  from featuretools.utils.gen_utils import Library, import_or_none
  from featuretools.utils.spark_utils import pd_to_spark_clean
label_desc: Use image node_id 3 for calling a global function with example usage: test_create_entityset_with_mixed_dataframe_types(pd_es, spark_es) without return types