class_docstr: Poisson likelihood for use with count data, where the rate is given by the (transformed) GP.
  
  let g(.) be the inverse-link function, then this likelihood represents
  
  p(yᵢ | fᵢ) = Poisson(yᵢ | g(fᵢ) * binsize)
  
  Note:binsize
  For use in a Log Gaussian Cox process (doubly stochastic model) where the
  rate function of an inhomogeneous Poisson process is given by a GP.  The
  intractable likelihood can be approximated via a Riemann sum (with bins
  of size 'binsize') and using this Poisson likelihood.
code: def _scalar_log_prob(
      self, X: TensorType, F: TensorType, Y: TensorType
  ) -> tf.Tensor:
      return logdensities.poisson(Y, self.invlink(F) * self.binsize)
  
imports: from typing import Any, Callable
  import numpy
  import tensorflow
  from check_shapes import check_shapes, inherit_check_shapes
  from ..None import logdensities
  from ..base import AnyNDArray, MeanAndVariance, Parameter, TensorType
  from ..config import default_float
  from ..utilities import positive, to_default_int
  from .base import ScalarLikelihood
  from .utils import inv_probit
label_desc: Use image node_id 2 for calling the Poisson obj's underlying member method code with example usage: obj._scalar_log_prob(X, F, Y) and returns: logdensities