class_docstr: Output layer using a quantile loss and projection layer to connect the
  quantile output to the network.
  
  Parameters
  ----------
  quantiles
      list of quantiles to compute loss over.
  
  quantile_weights
      weights of the quantiles.
class_docstr_tok: ["Output","layer","using","a","quantile","loss","and","projection","layer","to","connect","the","quantile","output","to","the","network",".","Parameters","--","--","--","--","--","quantiles","list","of","quantiles","to","compute","loss","over",".","quantile_weights","weights","of","the","quantiles","."]
code: def __init__(
      self,
      quantiles: List[float],
      quantile_weights: Optional[List[float]] = None,
  ) -> None:
      self._quantiles = quantiles
      self.num_quantiles = len(self._quantiles)
      self.quantile_weights = quantile_weights
  
imports: from typing import List, Optional
  from mxnet.gluon import nn
  from mxnet.gluon.loss import Loss
  from gluonts.core.component import validated
  from gluonts.mx import Tensor
label_desc: Use image node_id 1 to create a new QuantileOutput object  with example: obj = QuantileOutput(quantiles, quantile_weights)