class_docstr: Computes the quantile loss.
  
  Parameters
  ----------
  quantiles: The quantiles.
code: def update(
      self,
      y_pred: torch.Tensor,
      y_true: torch.Tensor,
      mask: torch.Tensor,
  ) -> None:
      # crop y_pred to shape of y_true since the model always predicts the max prediction length
      # max prediction length might be longer than max length in the particular batch
      y_pred = y_pred[:, : y_true.size()[1], ...]
      y_true = y_true.unsqueeze(-1)
      losses = torch.sum(
          torch.abs(
              (y_pred - y_true)
              * ((y_true <= y_pred).type(torch.uint8) - self.quantiles)
          ),
          axis=-1,
      )
      # mask out all elements that correspond to padding in y_true
      losses = torch.mul(losses, mask)
      self.quantile_sum += losses.sum()
      self.num_observations += mask.sum() * len(self.quantiles)
  
imports: from typing import List, Optional, Callable, Any
  import torch
  from torchmetrics import Metric
label_desc: Use image node_id 2 for calling the QuantileLoss obj's underlying member method code with example usage: obj.update(y_pred, y_true, mask) without return types