# Buggy code
def forward(self, x):
    if x.ndimension() == 1:
        x = x.unsqueeze(-1)
    elif x.ndimension() != 2:
        raise RuntimeError(
            "AdditiveGridInterpolationVariationalStrategy expects a 2d tensor."
        )

    num_data, num_dim = x.size()
    if num_dim != self.num_dim:
        raise RuntimeError("The number of dims should match the number specified.")

    output = super(AdditiveGridInterpolationVariationalStrategy, self).forward(x)
    if self.sum_output:
        mean = output.mean.sum(0)
        covar = output.lazy_covariance_matrix.sum(-3)
        return MultivariateNormal(mean, covar)
    else:
        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 x.ndimension() == 1:
        x = x.unsqueeze(-1)
    elif x.ndimension() != 2:
        raise RuntimeError(
            "AdditiveGridInterpolationVariationalStrategy expects a 2d tensor."
        )

    num_data, num_dim = x.size()
    if num_dim != self.num_dim:
        raise RuntimeError("The number of dims should match the number specified.")

    output = super().forward(
        x, inducing_points, inducing_values, variational_inducing_covar
    )
    if self.sum_output:
        if variational_inducing_covar is not None:
            mean = output.mean.sum(0)
            covar = output.lazy_covariance_matrix.sum(-3)
            return MultivariateNormal(mean, covar)
        else:
            return Delta(output.mean.sum(0))
    else:
        return output