docstr: Represent the quantile loss used to fit decoders that learn quantiles.
  
  Parameters
  ----------
  quantiles
      list of quantiles to compute loss over.
  quantile_weights
      weights of the quantiles.
  weight
      weighting of the loss.
  batch_axis
      indicates axis that represents the batch.
docstr_tok: ["Represent","the","quantile","loss","used","to","fit","decoders","that","learn","quantiles",".","Parameters","--","--","--","--","--","quantiles","list","of","quantiles","to","compute","loss","over",".","quantile_weights","weights","of","the","quantiles",".","weight","weighting","of","the","loss",".","batch_axis","indicates","axis","that","represents","the","batch","."]
code: def __init__(
      self,
      quantiles: List[float],
      quantile_weights: Optional[List[float]] = None,
      weight: Optional[float] = None,
      batch_axis: int = 0,
      **kwargs,
  ) -> None:
      """
      Represent the quantile loss used to fit decoders that learn quantiles.
  
      Parameters
      ----------
      quantiles
          list of quantiles to compute loss over.
      quantile_weights
          weights of the quantiles.
      weight
          weighting of the loss.
      batch_axis
          indicates axis that represents the batch.
      """
      assert len(quantiles) > 0
  
      super().__init__(weight, batch_axis, **kwargs)
  
      self.quantiles = quantiles
      self.num_quantiles = len(quantiles)
      self.quantile_weights = (
          quantile_weights
          if quantile_weights is not None
          else uniform_weights(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 1 to create a new QuantileLoss object from inherited base classes: Loss with example: obj = QuantileLoss(quantiles, quantile_weights, weight, batch_axis)