class_docstr: Implementation of Poisson likelihood, which is used for count data.
  
  Poisson likelihood uses the :class:`~pyro.distributions.Poisson`
  distribution, so the output of ``response_function`` should be positive.
  By default, we use :func:`torch.exp` as response function, corresponding
  to a log-Gaussian Cox process.
  
  :param callable response_function: A mapping to positive real numbers.
code: def __init__(self, response_function=None):
      super().__init__()
      self.response_function = (
          torch.exp if response_function is None else response_function
      )
  
file_path: pyro/pyro/contrib/gp/likelihoods/poisson.py
imports: import torch
  import pyro
  import pyro.distributions
  from pyro.contrib.gp.likelihoods.likelihood import Likelihood
label_desc: Use image node_id 1 to create a new Poisson object from inherited base classes: Likelihood with example: obj = Poisson(response_function)