code: def validate_forecast(
      forecast: Forecast, quantiles: Iterable[Quantile]
  ) -> bool:
      """Validates a Forecast object by checking it for `NaN` values.
      The supplied quantiles and mean (if available) are checked.
  
      Parameters
      ----------
      forecast
          The forecast object.
      quantiles
          List of strings of the form 'p10' or floats in [0, 1] with
          the quantile levels.
  
      Returns
      -------
          True, if the forecast's mean and quantiles have no `NaN` values,
          else False.
      """
      try:
          mean_fcst = getattr(forecast, "mean", None)
      except NotImplementedError:
          mean_fcst = None
  
      valid = (
          ~np.isnan(mean_fcst).any() if mean_fcst is not None else True
      )
      valid &= all(
          ~np.isnan(forecast.quantile(q.value)).any() for q in quantiles
      )
  
      return valid
  
imports: import logging
  import multiprocessing
  import sys
  from functools import partial
  from itertools import chain, tee
  from typing import Any, Callable, Dict, Iterable, Iterator, List, Optional, Tuple, Union, Mapping, cast
  import numpy
  import pandas
  from gluonts.gluonts_tqdm import tqdm
  from gluonts.model.forecast import Forecast, Quantile
  from gluonts.time_feature import get_seasonality
  from .metrics import abs_error, abs_target_mean, abs_target_sum, calculate_seasonal_error, coverage, mape, mase, mse, msis, quantile_loss, smape, num_masked_values
label_desc: Use image node_id 5 for calling a global function with example usage: validate_forecast(forecast, quantiles) and returns: valid