class_docstr: Class to construct a distribution given the output of a network.
docstr: Shape of each individual event contemplated by the distributions
  that this object constructs.
docstr_tok: ["Shape","of","each","individual","event","contemplated","by","the","distributions","that","this","object","constructs","."]
code: def event_shape(self) -> Tuple:
      r"""
      Shape of each individual event contemplated by the distributions
      that this object constructs.
      """
      raise NotImplementedError()
  
imports: from typing import Dict, Optional, Tuple
  import numpy
  import torch
  import torch.nn.functional
  from torch.distributions import Beta, Distribution, Gamma, Laplace, Normal, Poisson
  from gluonts.core.component import validated
  from gluonts.model.forecast_generator import DistributionForecastGenerator, ForecastGenerator
  from gluonts.torch.distributions import AffineTransformed
  from .output import Output
label_desc: Use image node_id 5 for calling the DistributionOutput obj's underlying member method code with example usage: obj.event_shape() without return types