# Buggy code
def forward(self, x):
    variational_distribution = self.variational_distribution.variational_distribution

    # Get interpolations
    interp_indices, interp_values = self._compute_grid(x)

    # Compute test mean
    # Left multiply samples by interpolation matrix
    predictive_mean = left_interp(
        interp_indices, interp_values, variational_distribution.mean.unsqueeze(-1)
    )
    predictive_mean = predictive_mean.squeeze(-1)

    # Compute test covar
    predictive_covar = InterpolatedLazyTensor(
        variational_distribution.lazy_covariance_matrix,
        interp_indices,
        interp_values,
        interp_indices,
        interp_values,
    )

    output = MultivariateNormal(predictive_mean, predictive_covar)
    return output


# 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, x, inducing_points, inducing_values, variational_inducing_covar=None):
    if variational_inducing_covar is None:
        raise RuntimeError(
            "GridInterpolationVariationalStrategy is only compatible with Gaussian variational "
            f"distributions. Got ({self.variational_distribution.__class__.__name__}."
        )

    variational_distribution = self.variational_distribution

    # Get interpolations
    interp_indices, interp_values = self._compute_grid(x)

    # Compute test mean
    # Left multiply samples by interpolation matrix
    predictive_mean = left_interp(
        interp_indices, interp_values, inducing_values.unsqueeze(-1)
    )
    predictive_mean = predictive_mean.squeeze(-1)

    # Compute test covar
    predictive_covar = InterpolatedLazyTensor(
        variational_distribution.lazy_covariance_matrix,
        interp_indices,
        interp_values,
        interp_indices,
        interp_values,
    )
    output = MultivariateNormal(predictive_mean, predictive_covar)
    return output