# Buggy code
def expected_log_prob(self, observations, function_dist, *params, **kwargs):
    if torch.any(observations.eq(-1)):
        warnings.warn(
            "BernoulliLikelihood.expected_log_prob expects observations with labels in {0, 1}. "
            "Observations with labels in {-1, 1} are deprecated.",
            DeprecationWarning,
        )
    else:
        observations = observations.mul(2).sub(1)
    # Custom function here so we can use log_normal_cdf rather than Normal.cdf
    # This is going to be less prone to overflow errors
    log_prob_lambda = lambda function_samples: log_normal_cdf(
        function_samples.mul(observations)
    )
    log_prob = self.quadrature(log_prob_lambda, function_dist)
    return log_prob.sum(-1)


# Traceback (AttributeError)
---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-6-c955f39ee560> in <module>
4 with torch.no_grad():
5     for x_batch, y_batch in test_loader:
----> 6         preds = model(x_batch)
7         means = torch.cat([means, preds.mean.cpu()])
8 means = means[1:]

~/miniconda3/envs/mbrl/lib/python3.7/site-packages/gpytorch/models/abstract_variational_gp.py in __call__(self, inputs, **kwargs)
20             inputs = inputs.unsqueeze(-1)
21
---> 22         return self.variational_strategy(inputs)

~/miniconda3/envs/mbrl/lib/python3.7/site-packages/gpytorch/variational/variational_strategy.py in __call__(self, x)
228                 self._memoize_cache = dict()
229
--> 230         return super(VariationalStrategy, self).__call__(x)

~/miniconda3/envs/mbrl/lib/python3.7/site-packages/gpytorch/module.py in __call__(self, *inputs, **kwargs)
20
21     def __call__(self, *inputs, **kwargs):
---> 22         outputs = self.forward(*inputs, **kwargs)
23         if isinstance(outputs, list):
24             return [_validate_module_outputs(output) for output in outputs]

~/miniconda3/envs/mbrl/lib/python3.7/site-packages/gpytorch/variational/variational_strategy.py in forward(self, x)
214                     induc_induc_covar.inv_matmul(induc_data_covar)
215                 )
--> 216                 data_covariance = data_data_covar + neg_induc_data_data_covar
217             predictive_covar = PsdSumLazyTensor(predictive_covar, data_covariance)
218

~/miniconda3/envs/mbrl/lib/python3.7/site-packages/gpytorch/lazy/sum_lazy_tensor.py in __add__(self, other)
75             return SumLazyTensor(*(list(self.lazy_tensors) + [other]))
76         else:
---> 77             raise AttributeError("other must be a LazyTensor")
78
79     def diag(self):

AttributeError: other must be a LazyTensor

# Fixed code
def expected_log_prob(self, observations, function_dist, *params, **kwargs):
    if torch.any(observations.eq(-1)):
        warnings.warn(
            "BernoulliLikelihood.expected_log_prob expects observations with labels in {0, 1}. "
            "Observations with labels in {-1, 1} are deprecated.",
            DeprecationWarning,
        )
    else:
        observations = observations.mul(2).sub(1)
    # Custom function here so we can use log_normal_cdf rather than Normal.cdf
    # This is going to be less prone to overflow errors
    log_prob_lambda = lambda function_samples: log_normal_cdf(
        function_samples.mul(observations)
    )
    log_prob = self.quadrature(log_prob_lambda, function_dist)
    return log_prob