# Buggy code
def __call__(self, input, *params, **kwargs):
    # Conditional
    if torch.is_tensor(input):
        return super().__call__(input, *params, **kwargs)
    # Marginal
    elif any(
        [
            isinstance(input, MultivariateNormal),
            isinstance(input, pyro.distributions.Normal),
            (
                isinstance(input, pyro.distributions.Independent)
                and isinstance(input.base_dist, pyro.distributions.Normal)
            ),
        ]
    ):
        return self.marginal(input, *params, **kwargs)
    # Error
    else:
        raise RuntimeError(
            "Likelihoods expects a MultivariateNormal or Normal input to make marginal predictions, or a "
            "torch.Tensor for conditional predictions. Got a {}".format(
                input.__class__.__name__
            )
        )


# 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 __call__(self, input, *args, **kwargs):
    # Conditional
    if torch.is_tensor(input):
        return super().__call__(input, *args, **kwargs)
    # Marginal
    elif any(
        [
            isinstance(input, MultivariateNormal),
            isinstance(input, pyro.distributions.Normal),
            (
                isinstance(input, pyro.distributions.Independent)
                and isinstance(input.base_dist, pyro.distributions.Normal)
            ),
        ]
    ):
        return self.marginal(input, *args, **kwargs)
    # Error
    else:
        raise RuntimeError(
            "Likelihoods expects a MultivariateNormal or Normal input to make marginal predictions, or a "
            "torch.Tensor for conditional predictions. Got a {}".format(
                input.__class__.__name__
            )
        )