# Buggy code
def forward(self, output, target, *params):
    if not isinstance(output, MultivariateNormal):
        raise RuntimeError(
            "ExactMarginalLogLikelihood can only operate on Gaussian random variables"
        )

    # Get the log prob of the marginal distribution
    output = self.likelihood(output, *params)
    res = output.log_prob(target)

    # Add additional terms (SGPR / learned inducing points, heteroskedastic likelihood models)
    for added_loss_term in self.model.added_loss_terms():
        res = res.add(added_loss_term.loss(*params))

    # Add log probs of priors on the (functions of) parameters
    for _, prior, closure, _ in self.named_priors():
        res.add_(prior.log_prob(closure()).sum())

    # Scale by the amount of data we have
    num_data = target.size(-1)
    return res.div_(num_data)


# 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 forward(self, function_dist, target, *params):
    r"""
    Computes the MLL given :math:`p(\mathbf f)` and :math:`\mathbf y`.

    :param ~gpytorch.distributions.MultivariateNormal function_dist: :math:`p(\mathbf f)`
        the outputs of the latent function (the :obj:`gpytorch.models.ExactGP`)
    :param torch.Tensor target: :math:`\mathbf y` The target values
    :rtype: torch.Tensor
    :return: Exact MLL. Output shape corresponds to batch shape of the model/input data.
    """
    if not isinstance(function_dist, MultivariateNormal):
        raise RuntimeError(
            "ExactMarginalLogLikelihood can only operate on Gaussian random variables"
        )

    # Get the log prob of the marginal distribution
    output = self.likelihood(function_dist, *params)
    res = output.log_prob(target)

    # Add additional terms (SGPR / learned inducing points, heteroskedastic likelihood models)
    for added_loss_term in self.model.added_loss_terms():
        res = res.add(added_loss_term.loss(*params))

    # Add log probs of priors on the (functions of) parameters
    for _, prior, closure, _ in self.named_priors():
        res.add_(prior.log_prob(closure()).sum())

    # Scale by the amount of data we have
    num_data = target.size(-1)
    return res.div_(num_data)