class_docstr: A class representing probability distributions.
class_docstr_tok: ["A","class","representing","probability","distributions","."]
docstr: Shape of each individual event contemplated by the distribution.
  
  For example, distributions over scalars have `event_shape = ()`,
  over vectors have `event_shape = (d, )` where `d` is the length
  of the vectors, over matrices have `event_shape = (d1, d2)`, and
  so on.
  
  Invoking `sample()` from a distribution yields a tensor of shape
  `batch_shape + event_shape`.
  
  This property is available in general only in mx.ndarray mode,
  when the shape of the distribution arguments can be accessed.
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code: def event_shape(self) -> Tuple:
      r"""
      Shape of each individual event contemplated by the distribution.
  
      For example, distributions over scalars have `event_shape = ()`,
      over vectors have `event_shape = (d, )` where `d` is the length
      of the vectors, over matrices have `event_shape = (d1, d2)`, and
      so on.
  
      Invoking `sample()` from a distribution yields a tensor of shape
      `batch_shape + event_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 8 for calling the Distribution obj's underlying member method code with example usage: obj.event_shape() without return types