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","."]
code: def __init__(
      self,
      quantiles: List[float],
      quantile_weights: Optional[List[float]] = None,
  ) -> None:
      super().__init__(sorted(quantiles), 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 IncrementalQuantileOutput object from inherited base classes: QuantileOutput with example: obj = IncrementalQuantileOutput(quantiles, quantile_weights)