class_docstr: A Forecast that contains arrays (i.e. time series) for quantiles and mean.
  
  Parameters
  ----------
  forecast_arrays
      An array of forecasts
  start_date
      start of the forecast
  forecast_keys
      A list of quantiles of the form '0.1', '0.9', etc.,
      and potentially 'mean'. Each entry corresponds to one array in
      forecast_arrays.
  item_id
      Identifier of the item being forecasted.
  info
      Additional information that the forecaster may provide e.g. estimated
      parameters, number of iterations ran etc.
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code: def __repr__(self):
      return ", ".join(
          [
              f"QuantileForecast({self.forecast_array!r})",
              f"start_date={self.start_date!r}",
              f"forecast_keys={self.forecast_keys!r}",
              f"item_id={self.item_id!r}",
              f"info={self.info!r})",
          ]
      )
  
imports: import re
  import logging
  from dataclasses import field
  from typing import Callable, Dict, List, Optional, Union, Tuple
  import numpy
  import pandas
  from gluonts.core.component import validated
  from gluonts.pydantic import dataclass
  from gluonts import maybe
label_desc: Use image node_id 6 for calling the QuantileForecast obj's underlying member method code with example usage: obj.__repr__() and returns: str