class_docstr: Create a Poisson distribution object.
  
  Parameters
  ----------
  rate : Tensor or scalar, default 1
      rate parameter of the distribution.
code: def __init__(self, rate=1.0, validate_args=None):
      self.rate = rate
      super(Poisson, self).__init__(
          event_dim=0, validate_args=validate_args
      )
  
file_path: mxnet/python/mxnet/gluon/probability/distributions/poisson.py
imports: from numbers import Number
  from .exp_family import ExponentialFamily
  from .constraint import Positive, NonNegativeInteger
  from .utils import gammaln
  from ....None import np, npx
label_desc: Use image node_id 1 to create a new Poisson object from inherited base classes: ExponentialFamily with example: obj = Poisson(rate, validate_args)