class_docstr: A `Forecast` object that uses a GluonTS distribution directly. This can for
  instance be used to represent marginal probability distributions for each
  time point -- although joint distributions are also possible, e.g. when
  using MultiVariateGaussian).
  
  Parameters
  ----------
  distribution
      Distribution object. This should represent the entire prediction
      length, i.e., if we draw `num_samples` samples from the distribution,
      the sample shape should be
  
          samples = trans_dist.sample(num_samples)
          samples.shape -> (num_samples, prediction_length)
  
  start_date
      start of the forecast
  info
      additional information that the forecaster may provide e.g. estimated
      parameters, number of iterations ran etc.
code: def to_quantile_forecast(self, quantiles: List[Union[float, str]]):
      return QuantileForecast(
          forecast_arrays=np.array(
              [self.quantile(q) for q in quantiles]
          ),
          forecast_keys=[
              str(Quantile.parse(level)) for level in quantiles
          ],
          start_date=self.start_date,
          item_id=self.item_id,
          info=self.info,
      )
  
file_path: gluonts/src/gluonts/mx/model/forecast.py
imports: from typing import Dict, List, Optional, Union
  import mxnet
  import numpy
  import pandas
  from gluonts.core.component import validated
  from gluonts.model.forecast import Forecast, Quantile, QuantileForecast, SampleForecast
  from gluonts.mx.distribution import Distribution
label_desc: Use image node_id 6 for calling the DistributionForecast obj's underlying member method code with example usage: obj.to_quantile_forecast(quantiles) and returns: QuantileForecast