# Buggy code
def __init__(self, mean, covariance_matrix, validate_args=False):
    self._islazy = isinstance(mean, LazyTensor) or isinstance(
        covariance_matrix, LazyTensor
    )
    if self._islazy:
        if validate_args:
            # TODO: add argument validation
            raise NotImplementedError()
        self.loc = mean
        self._covar = covariance_matrix
        self.__unbroadcasted_scale_tril = None
        self._validate_args = validate_args
        batch_shape, event_shape = self.loc.shape[:-1], self.loc.shape[-1:]
        # TODO: Integrate argument validation for LazyTensors into torch.distribution validation logic
        super(TMultivariateNormal, self).__init__(
            batch_shape, event_shape, validate_args=False
        )
    else:
        super().__init__(
            loc=mean, covariance_matrix=covariance_matrix, validate_args=validate_args
        )


# 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 __init__(self, mean, covariance_matrix, validate_args=False):
    self._islazy = isinstance(mean, LazyTensor) or isinstance(
        covariance_matrix, LazyTensor
    )
    if self._islazy:
        if validate_args:
            # TODO: add argument validation
            raise NotImplementedError()
        self.loc = mean
        self._covar = covariance_matrix
        self.__unbroadcasted_scale_tril = None
        self._validate_args = validate_args
        batch_shape = _mul_broadcast_shape(
            self.loc.shape[:-1], covariance_matrix.shape[:-2]
        )
        event_shape = self.loc.shape[-1:]
        # TODO: Integrate argument validation for LazyTensors into torch.distribution validation logic
        super(TMultivariateNormal, self).__init__(
            batch_shape, event_shape, validate_args=False
        )
    else:
        super().__init__(
            loc=mean, covariance_matrix=covariance_matrix, validate_args=validate_args
        )