class_docstr: A class representing probability distributions.
class_docstr_tok: ["A","class","representing","probability","distributions","."]
docstr: Layout of the set of events contemplated by the distribution.
  
  Invoking `sample()` from a distribution yields a tensor of shape
  `batch_shape + event_shape`, and computing `log_prob` (or `loss`
  more in general) on such sample will yield a tensor of shape
  `batch_shape`.
  
  This property is available in general only in mx.ndarray mode,
  when the shape of the distribution arguments can be accessed.
docstr_tok: ["Layout","of","the","set","of","events","contemplated","by","the","distribution",".","Invoking","`","sample","(",")","`","from","a","distribution","yields","a","tensor","of","shape","`","batch_shape","+","event_shape","`",",","and","computing","`","log_prob","`","(","or","`","loss","`","more","in","general",")","on","such","sample","will","yield","a","tensor","of","shape","`","batch_shape","`",".","This","property","is","available","in","general","only","in","mx.ndarray","mode",",","when","the","shape","of","the","distribution","arguments","can","be","accessed","."]
code: def batch_shape(self) -> Tuple:
      r"""
      Layout of the set of events contemplated by the distribution.
  
      Invoking `sample()` from a distribution yields a tensor of shape
      `batch_shape + event_shape`, and computing `log_prob` (or `loss`
      more in general) on such sample will yield a tensor of shape
      `batch_shape`.
  
      This property is available in general only in mx.ndarray mode,
      when the shape of the distribution arguments can be accessed.
      """
      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 7 for calling the Distribution obj's underlying member method code with example usage: obj.batch_shape() without return types