class_docstr: Quantile loss, i.e. a quantile of ``q=0.5`` will give half of the mean absolute error as it is calculated as
  
  Defined as ``max(q * (y-y_pred), (1-q) * (y_pred-y))``
docstr: Quantile loss
  
  Args:
      quantiles: quantiles for metric
docstr_tok: ["Quantile","loss","Args",":","quantiles",":","quantiles","for","metric"]
code: def __init__(
      self,
      quantiles: List[float] = [0.02, 0.1, 0.25, 0.5, 0.75, 0.9, 0.98],
      **kwargs,
  ):
      """
      Quantile loss
  
      Args:
          quantiles: quantiles for metric
      """
      super().__init__(quantiles=quantiles, **kwargs)
  
file_path: pytorch-forecasting/pytorch_forecasting/metrics/quantile.py
imports: from typing import Any, Callable, Dict, List, Optional, Tuple, Union
  import torch
  from pytorch_forecasting.metrics.base_metrics import MultiHorizonMetric
label_desc: Use image node_id 1 to create a new QuantileLoss object from inherited base classes: MultiHorizonMetric with example: obj = QuantileLoss(quantiles)