docstr: Compute the quantile loss of the given quantile.
  
  Parameters
  ----------
  F
      A module that can either refer to the Symbol API or the NDArray
      API in MXNet.
  y_true
      ground truth values to compute the loss against.
  y_pred_p
      predicted target quantile, same shape as ``y_true``.
  p
      quantile error to compute the loss.
  
  Returns
  -------
  Tensor
      quantile loss, shape: (N1 x N2 x ... x Nk x 1)
docstr_tok: ["Compute","the","quantile","loss","of","the","given","quantile",".","Parameters","--","--","--","--","--","F","A","module","that","can","either","refer","to","the","Symbol","API","or","the","NDArray","API","in","MXNet",".","y_true","ground","truth","values","to","compute","the","loss","against",".","y_pred_p","predicted","target","quantile",",","same","shape","as","``","y_true","``",".","p","quantile","error","to","compute","the","loss",".","Returns","--","--","--","-","Tensor","quantile","loss",",","shape",":","(","N1","x","N2","x","...","x","Nk","x","1",")"]
code: def compute_quantile_loss(
      F, y_true: Tensor, y_pred_p: Tensor, p: float
  ) -> Tensor:
      """
      Compute the quantile loss of the given quantile.
  
      Parameters
      ----------
      F
          A module that can either refer to the Symbol API or the NDArray
          API in MXNet.
      y_true
          ground truth values to compute the loss against.
      y_pred_p
          predicted target quantile, same shape as ``y_true``.
      p
          quantile error to compute the loss.
  
      Returns
      -------
      Tensor
          quantile loss, shape: (N1 x N2 x ... x Nk x 1)
      """
  
      under_bias = p * F.maximum(y_true - y_pred_p, 0)
      over_bias = (1 - p) * F.maximum(y_pred_p - y_true, 0)
  
      qt_loss = 2 * (under_bias + over_bias)
  
      return qt_loss
  
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 3 for calling the QuantileLoss obj's underlying member method code with example usage: obj.compute_quantile_loss(F, y_true, y_pred_p, p) and returns: qt_loss