def initial_C(measure, N, dtype=torch.float):
    """Return C that captures the other endpoint in the HiPPO approximation."""

    if measure == 'legt':
        C = (torch.arange(N, dtype=dtype)*2+1)**.5 * (-1)**torch.arange(N)
    elif measure == 'fourier':
        C = torch.zeros(N)
        C[0::2] = 2**.5
        C[0] = 1
    else:
        C = torch.zeros(N, dtype=dtype) # (N)

    return C
Return C that captures the other endpoint in the HiPPO approximation.
analyze/convnexts4nd/src/models/hippo/hippo.py