class_docstr: Poisson distribution, i.e. the distribution of the number of
  successes in a specified region.
  
  Parameters
  ----------
  rate
      Tensor containing the means, of shape `(*batch_shape, *event_shape)`.
  F
class_docstr_tok: ["Poisson","distribution",",","i.e",".","the","distribution","of","the","number","of","successes","in","a","specified","region",".","Parameters","--","--","--","--","--","rate","Tensor","containing","the","means",",","of","shape","`","(","*","batch_shape",",","*","event_shape",")","`",".","F"]
code: def event_dim(self) -> int:
      return 0
  
imports: from typing import Dict, List, Optional, Tuple
  import numpy
  from gluonts.core.component import validated
  from gluonts.mx import Tensor
  from .deterministic import DeterministicOutput
  from .distribution import Distribution, _sample_multiple, getF, softplus
  from .distribution_output import DistributionOutput
  from .mixture import MixtureDistributionOutput
label_desc: Use image node_id 5 for calling the Poisson obj's underlying member method code with example usage: obj.event_dim() and returns: int