979b8c9efa551e8c948a4aca145367a2d87ac8d6,gpytorch/distributions/multitask_multivariate_normal.py,MultitaskMultivariateNormal,rsample,#MultitaskMultivariateNormal#Any#Any#,95

Before Change

                    "of self.mean. Expected ...{} but got {}".format(self.loc.size(), base_samples.size())
                )

            sample_shape = torch.Size(tuple(base_samples.size(i) for i in range(base_samples.dim() - self.mean.dim())))
            base_samples = base_samples.view(*sample_shape, *self.loc.shape)

        samples = super(MultitaskMultivariateNormal, self).rsample(sample_shape=sample_shape, base_samples=base_samples)

After Change

            // Make sure that the base samples agree with the distribution
            mean_shape = self.mean.shape
            base_sample_shape = base_samples.shape[-self.mean.ndimension() :]
            if mean_shape != base_sample_shape:
                raise RuntimeError(
                    "The shape of base_samples (minus sample shape dimensions) should agree with the shape "
                    "of self.mean. Expected ...{} but got {}".format(mean_shape, base_sample_shape)
                )
            sample_shape = base_samples.shape[: -self.mean.ndimension()]
            base_samples = base_samples.view(*sample_shape, *self.loc.shape)

        samples = super().rsample(sample_shape=sample_shape, base_samples=base_samples)

Instances