code: def split_dataframe_by_type(
      input_df: pd.DataFrame,
  ) -> Dict[str, pd.DataFrame]:
      if "type" in input_df.columns:
          return {
              dataset_type: input_df[input_df["type"] == dataset_type]
              for dataset_type in input_df["type"].unique()
          }
      else:
          return {"origin": input_df}
  
file_path: FATE/python/fate/components/components/evaluation.py
imports: import logging
  from typing import Dict
  from fate.arch import Context
  import numpy
  import pandas
  from fate.arch import Context
  from fate.components.core import ARBITER, GUEST, HOST, Role, cpn
  from fate.components.core.params import string_choice
  from fate.ml.evaluation.tool import get_binary_metrics, get_multi_metrics, get_regression_metrics, get_specified_metrics
  from fate.ml.utils.predict_tools import PREDICT_SCORE, PREDICT_RESULT, LABEL
  from fate.components.components.utils.consts import BINARY, REGRESSION, MULTI
label_desc: Use image node_id 1 for calling a global function with example usage: split_dataframe_by_type(input_df) and returns: unknown, dict