class_docstr: Same as mean_weighted_quantile_loss in meta.evaluation.metrics just for pytorch
  
  Parameters
  ----------
  quantiles: The quantiles.
code: def __init__(
      self,
      quantiles: List[str],
      rescale: bool = False,
      compute_on_step: bool = True,
      dist_sync_on_step: bool = False,
      process_group: Optional[Any] = None,
      dist_sync_fn: Callable = None,
  ):
      super().__init__(
          compute_on_step=compute_on_step,
          dist_sync_on_step=dist_sync_on_step,
          process_group=process_group,
          dist_sync_fn=dist_sync_fn,
      )
      self.register_buffer(
          "quantiles", torch.as_tensor([float(q) for q in quantiles])
      )
      self.rescale = rescale
      self.add_state(
          "quantile_losses",
          default=torch.as_tensor(0.0),
          dist_reduce_fx="sum",
      )
      self.add_state(
          "denom", default=torch.as_tensor(0.0), dist_reduce_fx="sum"
      )
  
imports: from typing import List, Optional, Callable, Any
  import torch
  from torchmetrics import Metric
label_desc: Use image node_id 1 to create a new CRPS object from inherited base classes: Metric with example: obj = CRPS(quantiles, rescale, compute_on_step, dist_sync_on_step, process_group, dist_sync_fn)