code: def quantile_loss(y_true, y_pred, q: float = 0.95):
      """Calculates the quantile (pinball) loss with quantile q of y_true and y_pred.
  
      :param y_true: one column of true values.
      :type y_true: list or numpy array
      :param y_pred: one column of predicted values.
      :type y_pred: list or numpy array.
      :param q: the quantile.
      :type q: float
      :return: quantile loss.
      :returns: float
      """
      return np.where(
          y_true < y_pred,
          (1 - q) * (y_pred - y_true),
          q * (y_true - y_pred),
      ).mean()
  
imports: import math
  import warnings
  from collections import namedtuple
  from enum import Enum
  from functools import partial
  from typing import List
  from typing import Optional
  from typing import Union
  import numpy
  import pandas
  from scipy.stats import pearsonr
  from sklearn.metrics import mean_absolute_error
  from sklearn.metrics import mean_squared_error
  from sklearn.metrics import median_absolute_error
  from sklearn.metrics import r2_score
  from greykite.common.constants import ACTUAL_COL
  from greykite.common.constants import COVERAGE_VS_INTENDED_DIFF
  from greykite.common.constants import LOWER_BAND_COVERAGE
  from greykite.common.constants import PREDICTED_COL
  from greykite.common.constants import PREDICTED_LOWER_COL
  from greykite.common.constants import PREDICTED_UPPER_COL
  from greykite.common.constants import PREDICTION_BAND_COVERAGE
  from greykite.common.constants import PREDICTION_BAND_WIDTH
  from greykite.common.constants import UPPER_BAND_COVERAGE
  from greykite.common.logging import LoggingLevelEnum
  from greykite.common.logging import log_message
label_desc: Use image node_id 13 for calling a global function with example usage: quantile_loss(y_true, y_pred, q) and returns: np