class_docstr: Cross entropy loss for classification.
class_docstr_tok: ["Cross","entropy","loss","for","classification","."]
docstr: Convert network prediction into a quantile prediction.
  
  Args:
      y_pred: prediction output of network
      quantiles (List[float], optional): quantiles for probability range. Defaults to quantiles as
          as defined in the class initialization.
  
  Returns:
      torch.Tensor: prediction quantiles
docstr_tok: ["Convert","network","prediction","into","a","quantile","prediction",".","Args",":","y_pred",":","prediction","output","of","network","quantiles","(","List","[","float","]",",","optional",")",":","quantiles","for","probability","range",".","Defaults","to","quantiles","as","as","defined","in","the","class","initialization",".","Returns",":","torch.Tensor",":","prediction","quantiles"]
code: def to_quantiles(
      self, y_pred: torch.Tensor, quantiles: List[float] = None
  ) -> torch.Tensor:
      """
      Convert network prediction into a quantile prediction.
  
      Args:
          y_pred: prediction output of network
          quantiles (List[float], optional): quantiles for probability range. Defaults to quantiles as
              as defined in the class initialization.
  
      Returns:
          torch.Tensor: prediction quantiles
      """
      return y_pred
  
imports: from typing import Any, Callable, Dict, List, Optional, Tuple, Union
  import scipy.stats
  import torch
  import torch.nn.functional
  from torch.nn.utils import rnn
  from pytorch_forecasting.metrics.base_metrics import MultiHorizonMetric
  from pytorch_forecasting.utils import create_mask, unpack_sequence, unsqueeze_like
label_desc: Use image node_id 3 for calling the CrossEntropy obj's underlying member method code with example usage: obj.to_quantiles(y_pred, quantiles) and returns: y_pred