class_docstr: Output layer using a quantile loss and projection layer to connect the
  quantile output to the network.
  
  Differently from ``QuantileOutput``, this class enforces the correct
  order relation between quantiles: this is done by parametrizing
  the increments between quantiles instead of the quantiles directly.
  
  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",".","Differently","from","``","QuantileOutput","``",",","this","class","enforces","the","correct","order","relation","between","quantiles",":","this","is","done","by","parametrizing","the","increments","between","quantiles","instead","of","the","quantiles","directly",".","Parameters","--","--","--","--","--","quantiles","list","of","quantiles","to","compute","loss","over",".","quantile_weights","weights","of","the","quantiles","."]
docstr: Returns
  -------
  nn.HybridBlock
      constructs quantile loss object.
docstr_tok: ["Returns","--","--","--","-","nn.HybridBlock","constructs","quantile","loss","object","."]
code: def get_loss(self) -> nn.HybridBlock:
      """
      Returns
      -------
      nn.HybridBlock
          constructs quantile loss object.
      """
      return QuantileLoss(
          quantiles=self.quantiles,
          quantile_weights=(
              self.quantile_weights
              if self.quantile_weights is not None
              else crps_weights_pwl(self.quantiles)
          ),
      )
  
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 2 for calling the IncrementalQuantileOutput obj's underlying member method code with example usage: obj.get_loss() and returns: QuantileLoss