class_docstr: A class representing probability distributions.
class_docstr_tok: ["A","class","representing","probability","distributions","."]
docstr: Compute the *continuous rank probability score* (CRPS) of `x` according
  to the distribution.
  
  Parameters
  ----------
  x
      Tensor of shape `(*batch_shape, *event_shape)`.
  
  Returns
  -------
  Tensor
      Tensor of shape `batch_shape` containing the CRPS score,
      according to the distribution, for each event in `x`.
docstr_tok: ["Compute","the","*","continuous","rank","probability","score","*","(","CRPS",")","of","`","x","`","according","to","the","distribution",".","Parameters","--","--","--","--","--","x","Tensor","of","shape","`","(","*","batch_shape",",","*","event_shape",")","`",".","Returns","--","--","--","-","Tensor","Tensor","of","shape","`","batch_shape","`","containing","the","CRPS","score",",","according","to","the","distribution",",","for","each","event","in","`","x","`","."]
code: def crps(self, x: Tensor) -> Tensor:
      r"""
      Compute the *continuous rank probability score* (CRPS) of `x` according
      to the distribution.
  
      Parameters
      ----------
      x
          Tensor of shape `(*batch_shape, *event_shape)`.
  
      Returns
      -------
      Tensor
          Tensor of shape `batch_shape` containing the CRPS score,
          according to the distribution, for each event in `x`.
      """
      raise NotImplementedError()
  
imports: from typing import cast, Any, List, Optional, Tuple
  import mxnet
  import numpy
  from mxnet import autograd
  from gluonts.mx import Tensor
label_desc: Use image node_id 4 for calling the Distribution obj's underlying member method code with example usage: obj.crps(x) without return types