class_docstr: Create a Poisson distribution object.
  
  Parameters
  ----------
  rate : Tensor or scalar, default 1
      rate parameter of the distribution.
code: def log_prob(self, value):
      if self._validate_args:
          self._validate_samples(value)
      lgamma = gammaln()
      rate = self.rate
      return value * np.log(rate) - rate - lgamma(value + 1)
  
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 7 for calling the Poisson obj's underlying member method code with example usage: obj.log_prob(value) and returns: unknown