log_noise = self.log_noise
p = params[0] if isinstance(params, list) else params
n = p.shape[-2] if len(p.shape) > 1 else p.shape[-1]
log_noise_diag = log_noise.repeat(n, 1)
return DiagLazyTensor(log_noise_diag)
class MultitaskHomoskedasticNoise(HomoskedasticNoise):
After Change
if log_noise.ndimension() > len(shape):
raise RuntimeError("Must provide batched input if in batch mode")
if log_noise.shape[-1] > 1: // deal with multi-task case
shape = shape + torch.Size([log_noise.shape[-1]])
log_noise_diag = log_noise.expand(shape)
return DiagLazyTensor(log_noise_diag)
class MultitaskHomoskedasticNoise(HomoskedasticNoise):
