docstr: If `save_transformed_metrics` is True, and target transformation does occur, then
  experiment metrics are calculated using the transformed targets and predictions, which is
  the form returned directly by a fitted model's `predict` method. For example, if target data
  is label-encoded, and an :class:`feature_engineering.EngineerStep` is used to one-hot encode
  the target, then metrics functions will receive the following as input:
  (one-hot-encoded targets, one-hot-encoded predictions).
  
  Conversely, if `save_transformed_metrics` is False, and target transformation does occur,
  then experiment metrics are calculated using the inverse of the transformed targets and
  predictions, which is same form as the original target data. Continuing the example of
  label-encoded target data, and an :class:`feature_engineering.EngineerStep` to one-hot
  encode the target, in this case, metrics functions will receive the following as input:
  (label-encoded targets, label-encoded predictions)
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code: def save_transformed_metrics(self) -> bool:
      """If `save_transformed_metrics` is True, and target transformation does occur, then
      experiment metrics are calculated using the transformed targets and predictions, which is
      the form returned directly by a fitted model's `predict` method. For example, if target data
      is label-encoded, and an :class:`feature_engineering.EngineerStep` is used to one-hot encode
      the target, then metrics functions will receive the following as input:
      (one-hot-encoded targets, one-hot-encoded predictions).
  
      Conversely, if `save_transformed_metrics` is False, and target transformation does occur,
      then experiment metrics are calculated using the inverse of the transformed targets and
      predictions, which is same form as the original target data. Continuing the example of
      label-encoded target data, and an :class:`feature_engineering.EngineerStep` to one-hot
      encode the target, in this case, metrics functions will receive the following as input:
      (label-encoded targets, label-encoded predictions)"""
      return self._save_transformed_metrics
  
imports: from hyperparameter_hunter.callbacks.bases import BaseCallback
  from hyperparameter_hunter.i_o.reporting import ReportingHandler
  from hyperparameter_hunter.keys.makers import CrossExperimentKeyMaker
  from hyperparameter_hunter.metrics import format_metrics
  from hyperparameter_hunter.sentinels import DatasetSentinel
  from hyperparameter_hunter.settings import G, ASSETS_DIRNAME, RESULT_FILE_SUB_DIR_PATHS
  from hyperparameter_hunter.utils.boltons_utils import remap
  from hyperparameter_hunter.utils.file_utils import make_dirs, ParametersFromFile
  from hyperparameter_hunter.utils.general_utils import Alias
  from hyperparameter_hunter.utils.result_utils import format_predictions, default_do_full_save
  from inspect import signature, isclass
  import numpy
  import os.path
  import pandas
  from pandas.api.types import is_numeric_dtype
  from typing import List, Optional, Tuple, Union
  from sklearn.model_selection import _split
label_desc: Use image node_id 23 for calling the Environment obj's underlying member method code with example usage: obj.save_transformed_metrics() and returns: self