class_docstr: Poisson distribution.
  
  The Poisson distribution is parameterized by an event `rate` parameter.
  
  #### Mathematical Details
  
  The probability mass function (pmf) is,
  
  ```none
  pmf(k; lambda, k >= 0) = (lambda^k / k!) / Z
  Z = exp(lambda).
  ```
  
  where `rate = lambda` and `Z` is the normalizing constant.
class_docstr_tok: ["Poisson","distribution",".","The","Poisson","distribution","is","parameterized","by","an","event","`","rate","`","parameter",".","#","#","#","#","Mathematical","Details","The","probability","mass","function","(","pmf",")","is",",","``","`","none","pmf","(","k",";","lambda",",","k",">","=","0",")","=","(","lambda^k","\/","k","!",")","\/","Z","Z","=","exp","(","lambda",")",".","``","`","where","`","rate","=","lambda","`","and","`","Z","`","is","the","normalizing","constant","."]
docstr: Log rate parameter.
docstr_tok: ["Log","rate","parameter","."]
code: def log_rate(self):
      """Log rate parameter."""
      return self._log_rate
  
imports: import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import softplus
  from tensorflow_probability.python.distributions import distribution
  from tensorflow_probability.python.internal import assert_util
  from tensorflow_probability.python.internal import batched_rejection_sampler
  from tensorflow_probability.python.internal import distribution_util
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import implementation_selection
  from tensorflow_probability.python.internal import parameter_properties
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import reparameterization
  from tensorflow_probability.python.internal import samplers
  from tensorflow_probability.python.internal import tensor_util
label_desc: Use image node_id 4 for calling the Poisson obj's underlying member method code with example usage: obj.log_rate() and returns: self