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: Convert network prediction into a point prediction.
  
  Args:
      y_pred: prediction output of network
  
  Returns:
      torch.Tensor: point prediction
docstr_tok: ["Convert","network","prediction","into","a","point","prediction",".","Args",":","y_pred",":","prediction","output","of","network","Returns",":","torch.Tensor",":","point","prediction"]
code: def to_prediction(self, y_pred: torch.Tensor) -> torch.Tensor:
      """
      Convert network prediction into a point prediction.
  
      Args:
          y_pred: prediction output of network
  
      Returns:
          torch.Tensor: point prediction
      """
      if y_pred.ndim == 3:
          idx = self.quantiles.index(0.5)
          y_pred = y_pred[..., idx]
      return y_pred
  
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 3 for calling the QuantileLoss obj's underlying member method code with example usage: obj.to_prediction(y_pred) and returns: y_pred