code: def get_score_func_with_aggregation(
      score_func,
      greater_is_better=None,
      agg_periods=None,
      agg_func=None,
      relative_error_tolerance=None,
  ):
      """Returns a score function that pre-aggregates inputs according to ``agg_func``,
      and filters out invalid true values before evaluation. This allows fitting
      the model at a granular level, yet evaluating at a coarser level.
  
      Also returns the proper direction and short name for the score function.
  
      Parameters
      ----------
      score_func : `str` or callable
          If callable, a function that maps two arrays to a number:
          ``(true, predicted) -> score``.
      greater_is_better : `bool`, default False
          True if ``score_func`` is a score function, meaning higher is better,
          and False if it is a loss function, meaning lower is better.
          Must be provided if ``score_func`` is a callable (custom function).
          Ignored if ``score_func`` is a string, because the direction is known.
      agg_periods : `int` or None, default None
          Number of periods to aggregate before evaluation.
          Model is fit at original frequency, and forecast is
          aggregated according to ``agg_periods``
          E.g. fit model on hourly data, and evaluate performance at daily level
          If None, does not apply aggregation
      agg_func : callable or None, default None
          Takes an array and returns a number, e.g. np.max, np.sum
          Used to aggregate data prior to evaluation (applied to actual and predicted)
          Ignored if ``agg_periods`` is None
      relative_error_tolerance : `float` or None, default None
          Threshold to compute the
          `~greykite.common.constants.FRACTION_OUTSIDE_TOLERANCE`
          metric, defined as the fraction of forecasted values whose relative
          error is strictly greater than ``relative_error_tolerance``.
          For example, 0.05 allows for 5% relative error.
          Required if ``score_func`` is
          `~greykite.common.constants.FRACTION_OUTSIDE_TOLERANCE`.
  
      Returns
      -------
      score_func : callable
          scorer with pre-aggregation function and filter,
      greater_is_better : `bool`
          Whether ``greater_is_better`` for the scorer.
          Uses the provided ``greater_is_better`` if the
          provided ``score_func`` is a callable.
          Otherwise, looks up the direction.
      short_name : `str`
          Canonical short name for the ``score_func``.
      """
      if isinstance(score_func, str):
          # If a string is provided, looks up the callable score_func and greater_is_better.
          # Otherwise, uses the args directly
          if score_func == FRACTION_OUTSIDE_TOLERANCE:
              if relative_error_tolerance is None:
                  raise ValueError(
                      "Must specify `relative_error_tolerance` to request "
                      "FRACTION_OUTSIDE_TOLERANCE as a metric."
                  )
              score_func = partial(
                  fraction_outside_tolerance,
                  rtol=relative_error_tolerance,
              )
              greater_is_better = False
              short_name = FRACTION_OUTSIDE_TOLERANCE_NAME
          else:
              try:
                  enum = EvaluationMetricEnum[score_func]
                  score_func = enum.get_metric_func()
                  greater_is_better = (
                      enum.get_metric_greater_is_better()
                  )
                  short_name = enum.get_metric_name()
              except KeyError:
                  valid_names = ", ".join(
                      EvaluationMetricEnum.__dict__["_member_names_"]
                  )
                  raise NotImplementedError(
                      f"Evaluation metric {score_func} is not available. Must be one of: {valid_names}"
                  )
      elif callable(score_func):
          # Uses the score_func and greater_is_better passed to the function
          short_name = CUSTOM_SCORE_FUNC_NAME
      else:
          raise ValueError(
              "`score_func` must be an `EvaluationMetricEnum` member name, "
              "FRACTION_OUTSIDE_TOLERANCE, or callable."
          )
  
      if agg_periods is not None:
          score_func = add_preaggregation_to_scorer(
              score_func, agg_periods, agg_func
          )
  
      # Filters out elements that can't be compared in ``y_true``
      score_func = add_finite_filter_to_scorer(score_func)
  
      return score_func, greater_is_better, short_name
  
imports: import math
  from functools import partial
  import pandas
  from sklearn.base import clone
  from sklearn.model_selection import ParameterGrid
  from sklearn.model_selection import RandomizedSearchCV
  from sklearn.pipeline import Pipeline
  from greykite.common import constants
  from greykite.common.constants import FRACTION_OUTSIDE_TOLERANCE
  from greykite.common.constants import TIME_COL
  from greykite.common.constants import VALUE_COL
  from greykite.common.evaluation import EvaluationMetricEnum
  from greykite.common.evaluation import add_finite_filter_to_scorer
  from greykite.common.evaluation import add_preaggregation_to_scorer
  from greykite.common.evaluation import fraction_outside_tolerance
  from greykite.common.logging import LoggingLevelEnum
  from greykite.common.logging import log_message
  from greykite.common.python_utils import get_integer
  from greykite.common.python_utils import unique_elements_in_list
  from greykite.common.time_properties_forecast import get_default_horizon_from_period
  from greykite.framework.constants import CUSTOM_SCORE_FUNC_NAME
  from greykite.framework.constants import CV_REPORT_METRICS_ALL
  from greykite.framework.constants import FRACTION_OUTSIDE_TOLERANCE_NAME
  from greykite.framework.output.univariate_forecast import UnivariateForecast
  from greykite.sklearn.estimator.simple_silverkite_estimator import SimpleSilverkiteEstimator
  from greykite.sklearn.sklearn_scorer import make_scorer_df
  from greykite.sklearn.transform.column_selector import ColumnSelector
  from greykite.sklearn.transform.difference_based_outlier_transformer import DifferenceBasedOutlierTransformer
  from greykite.sklearn.transform.drop_degenerate_transformer import DropDegenerateTransformer
  from greykite.sklearn.transform.dtype_column_selector import DtypeColumnSelector
  from greykite.sklearn.transform.normalize_transformer import NormalizeTransformer
  from greykite.sklearn.transform.null_transformer import NullTransformer
  from greykite.sklearn.transform.pandas_feature_union import PandasFeatureUnion
label_desc: Use image node_id 4 for calling a global function with example usage: get_score_func_with_aggregation(score_func, greater_is_better, agg_periods, agg_func, relative_error_tolerance) and returns: score_func,  greater_is_better,  short_name