text: import numpy as np
  import tensorflow as tf
  from gpflow.likelihoods import Likelihood
  from gpflow import logdensities
  from gpflow import transforms
  from gpflow import settings
  from gpflow.decors import params_as_tensors
  from gpflow.params import Parameter
  
  class Gaussian(Likelihood):
      def __init__(self, variance=1.0, **kwargs):
          super().__init__(**kwargs)
          self.variance = Parameter(
              variance, transform=transforms.positive, dtype=settings.float_type)
  
      @params_as_tensors
      def logp(self, F, Y):
          import pdb; pdb.set_trace()
          return logdensities.gaussian(Y, F, self.variance)
  
      @params_as_tensors
      def conditional_mean(self, F):  # pylint: disable=R0201
          return tf.identity(F)
  
      @params_as_tensors
      def conditional_variance(self, F):
          return tf.fill(tf.shape(F), tf.squeeze(self.variance))
  
      @params_as_tensors
      def predict_mean_and_var(self, Fmu, Fvar):
          return tf.identity(Fmu), Fvar + self.variance
  
      @params_as_tensors
      def predict_density(self, Fmu, Fvar, Y):
          return logdensities.gaussian(Y, Fmu, Fvar + self.variance)
  
      @params_as_tensors
      def variational_expectations(self, Fmu, Fvar, Y):
          return -0.5 * np.log(2 * np.pi) - 0.5 * tf.log(self.variance) \
                 - 0.5 * (tf.square(Y - Fmu) + Fvar) / self.variance
  