class_docstr: Base class for all evaluation metrics used in AutoGluon-TimeSeries.
  
  This object always returns the metric in greater-is-better format.
  
  Follows the design of ``autogluon.core.metrics.Scorer``.
  
  Attributes
  ----------
  greater_is_better_internal : bool, default = False
      Whether internal method :meth:`~autogluon.timeseries.metrics.TimeSeriesScorer.compute_metric` is
      a loss function (default), meaning low is good, or a score function, meaning high is good.
  optimum : float, default = 0.0
      The best score achievable by the score function, i.e. maximum in case of scorer function and minimum in case of
      loss function.
  optimized_by_median : bool, default = False
      Whether given point forecast metric is optimized by the median (if True) or expected value (if False). If True,
      all models in AutoGluon-TimeSeries will attempt to paste median forecast into the "mean" column.
  needs_quantile : bool, default = False
      Whether the given metric uses the quantile predictions. Some models will modify the training procedure if they
      are trained to optimize a quantile metric.
  equivalent_tabular_regression_metric : str
      Name of an equivalent metric used by AutoGluon-Tabular with ``problem_type="regression"``. Used by models that
      train a TabularPredictor under the hood. This attribute should only be specified by point forecast metrics.
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docstr: Get inputs necessary to compute quantile forecast metrics.
  
  Returns
  -------
  y_true : pd.Series, shape [num_items * prediction_length]
      Target time series values during the forecast horizon.
  q_pred : pd.DataFrame, shape [num_items * prediction_length, num_quantiles]
      Quantile forecast for each predicted quantile level. Column order corresponds to ``quantile_levels``.
  quantile_levels : np.ndarray, shape [num_quantiles]
      Quantile levels for which the forecasts are generated (as floats).
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code: def _get_quantile_forecast_score_inputs(
      data_future: TimeSeriesDataFrame,
      predictions: TimeSeriesDataFrame,
      target: str = "target",
  ) -> Tuple[pd.Series, pd.DataFrame, np.ndarray]:
      """Get inputs necessary to compute quantile forecast metrics.
  
      Returns
      -------
      y_true : pd.Series, shape [num_items * prediction_length]
          Target time series values during the forecast horizon.
      q_pred : pd.DataFrame, shape [num_items * prediction_length, num_quantiles]
          Quantile forecast for each predicted quantile level. Column order corresponds to ``quantile_levels``.
      quantile_levels : np.ndarray, shape [num_quantiles]
          Quantile levels for which the forecasts are generated (as floats).
      """
      quantile_columns = [
          col for col in predictions.columns if col != "mean"
      ]
      y_true = data_future[target]
      q_pred = pd.DataFrame(predictions[quantile_columns])
      quantile_levels = np.array(quantile_columns, dtype=float)
      return y_true, q_pred, quantile_levels
  
file_path: autogluon/timeseries/src/autogluon/timeseries/metrics/abstract.py
imports: from typing import Optional, Tuple
  import numpy
  import pandas
  from autogluon.timeseries import TimeSeriesDataFrame
  from autogluon.timeseries.utils.datetime import get_seasonality
  from autogluon.timeseries.utils.warning_filters import warning_filter
label_desc: Use image node_id 13 for calling the TimeSeriesScorer obj's underlying member method code with example usage: obj._get_quantile_forecast_score_inputs(data_future, predictions, target) and returns: y_true,  q_pred,  quantile_levels