Wrong Example

[lakshya-4gp]

def hard_quantize(input: Tensor): """Applies binary quantization to all elements of the input tensor. Args: input (Tensor): input tensor Shapes:

• Input: :math:(*)

• Output: :math:(*) Examples::

  x = functional.random_hv(2, 3) y = functional.bundle(x[0], x[1]) y tensor([ 0., -2., -2.]) functional.hard_quantize(y) tensor([ 1., -1., -1.]) """

  Make sure that the output tensor has the same dtype and device

  as the input tensor.

  positive = torch.tensor(1.0, dtype=input.dtype, device=input.device) negative = torch.tensor(-1.0, dtype=input.dtype, device=input.device)

  return torch.where(input > 0, positive, negative)

The example given is wrong, since ( input > 0 ) is used the output of functional.hard_quantize(y) will be [-1, -1, -1]

[mikeheddes]

Thank you @lakshya-4gp for reporting this issue. I will look into it in the coming days.