class_docstr: Class to construct a distribution given the output of a network.
docstr: Number of event dimensions, i.e., length of the `event_shape` tuple,
  of the distributions that this object constructs.
docstr_tok: ["Number","of","event","dimensions",",","i.e.",",","length","of","the","`","event_shape","`","tuple",",","of","the","distributions","that","this","object","constructs","."]
code: def event_dim(self) -> int:
      r"""
      Number of event dimensions, i.e., length of the `event_shape` tuple,
      of the distributions that this object constructs.
      """
      return len(self.event_shape)
  
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 6 for calling the DistributionOutput obj's underlying member method code with example usage: obj.event_dim() and returns: len