code: def get_split_data_size(
      train_size, validate_size, test_size, data_count
  ):
      """
      Validate & transform param inputs into all int
      """
      # check & transform data set sizes
      if (
          isinstance(test_size, float)
          or isinstance(train_size, float)
          or isinstance(validate_size, float)
      ):
          total_size = 1.0
      else:
          total_size = data_count
      if train_size is None:
          if validate_size is None:
              train_size = total_size - test_size
              validate_size = total_size - (test_size + train_size)
          else:
              if test_size is None:
                  test_size = 0
              train_size = total_size - (validate_size + test_size)
      elif test_size is None:
          if validate_size is None:
              test_size = total_size - train_size
              validate_size = total_size - (test_size + train_size)
          else:
              test_size = total_size - (validate_size + train_size)
      elif validate_size is None:
          if train_size is None:
              train_size = total_size - test_size
          validate_size = total_size - (test_size + train_size)
  
      if (
          abs(
              (abs(train_size) + abs(test_size) + abs(validate_size))
              - total_size
          )
          > 1e-6
      ):
          raise ValueError(
              f"train_size, test_size, validate_size should sum up to 1.0 or data count"
          )
  
      if isinstance(train_size, float):
          train_size = round(train_size * data_count)
          validate_size = round(validate_size * data_count)
          test_size = total_size - train_size - validate_size
      return train_size, validate_size, test_size
  
file_path: FATE/python/fate/ml/model_selection/data_split.py
imports: import logging
  from fate.arch import Context
  from fate.arch.dataframe import DataFrame
  from ..abc.module import Module
label_desc: Use image node_id 3 for calling a global function with example usage: get_split_data_size(train_size, validate_size, test_size, data_count) and returns: train_size,  validate_size,  test_size