docstr: :param force_fixed_metric_expected_outputs: If True, the expected outputs provided at split time are used to compute the metric instead of their possibly modified version after passing through the pipeline. More info in the documentation of :func:`set_to_force_expected_outputs_for_scoring`.
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code: def __init__(self, force_fixed_metric_expected_outputs: bool = False):
      """
      :param force_fixed_metric_expected_outputs: If True, the expected outputs provided at split time are used to compute the metric instead of their possibly modified version after passing through the pipeline. More info in the documentation of :func:`set_to_force_expected_outputs_for_scoring`.
      """
      self.force_fixed_metric_expected_outputs: bool = False
  
imports: import copy
  import math
  from abc import ABC, abstractmethod
  from typing import Any, List, Optional, Set, Tuple
  import numpy
  from neuraxle.base import ExecutionContext
  from neuraxle.data_container import DIT, EOT, IDT, DACTData
  from neuraxle.data_container import DataContainer
  from neuraxle.data_container import TrainDACT, ValidDACT
label_desc: Use image node_id 1 to create a new BaseValidationSplitter object from inherited base classes: ABC with example: obj = BaseValidationSplitter(force_fixed_metric_expected_outputs)