class_docstr: Creates a Poisson distribution parameterized by :attr:`rate`, the rate parameter.
  
  Samples are nonnegative integers, with a pmf given by
  
  .. math::
    \mathrm{rate}^k \frac{e^{-\mathrm{rate}}}{k!}
  
  Example::
  
      >>> # xdoctest: +SKIP("poisson_cpu not implemented for 'Long'")
      >>> m = Poisson(torch.tensor([4]))
      >>> m.sample()
      tensor([ 3.])
  
  Args:
      rate (Number, Tensor): the rate parameter
code: def _log_normalizer(self, x):
      return torch.exp(x)
  
file_path: pytorch/torch/distributions/poisson.py
imports: from numbers import Number
  import torch
  from torch.distributions import constraints
  from torch.distributions.exp_family import ExponentialFamily
  from torch.distributions.utils import broadcast_all
label_desc: Use image node_id 9 for calling the Poisson obj's underlying member method code with example usage: obj._log_normalizer(x) and returns: torch