487ec4eacee294bf9ff630fc06db22fa19331b51,gpytorch/means/constant_mean_grad.py,ConstantMeanGrad,forward,#ConstantMeanGrad#Any#,15

Before Change

    def forward(self, input):
        mean = self.constant.squeeze().repeat(input.size(-2), input.size(-1) + 1)
        if input.ndimension() == 3:
            mean = self.constant.squeeze().repeat(input.size(0), input.size(1), input.size(2) + 1)
        else:
            mean = self.constant.squeeze().repeat(input.size(0), input.size(1) + 1)
        mean[..., :, 1:] = 0
        return mean

After Change

            self.register_prior("mean_prior", prior, "constant")

    def forward(self, input):
        batch_shape = _mul_broadcast_shape(self.batch_shape, input.shape[:-2])
        mean = self.constant.squeeze().repeat(*batch_shape, input.size(-2), input.size(-1) + 1)
        mean[..., :, 1:] = 0
        return mean

Instances