def unfold(a, kernel_size, stride):
    """Given input of size [*OT, T], output Tensor of size [*OT, F, K]
    with K the kernel size, by extracting frames with the given stride.

    This will pad the input so that `F = ceil(T / K)`.

    see https://github.com/pytorch/pytorch/issues/60466
    """
    *shape, length = a.shape
    n_frames = math.ceil(length / stride)
    tgt_length = (n_frames - 1) * stride + kernel_size
    a = F.pad(a, (0, tgt_length - length))
    strides = list(a.stride())
    assert strides[-1] == 1, 'data should be contiguous'
    strides = strides[:-1] + [stride, 1]
    return a.as_strided([*shape, n_frames, kernel_size], strides)
Given input of size [*OT, T], output Tensor of size [*OT, F, K]
with K the kernel size, by extracting frames with the given stride.

This will pad the input so that `F = ceil(T / K)`.

see https://github.com/pytorch/pytorch/issues/60466
demucs/utils.py