def init_ssm_real(self):
        """Returns (dense, real) (A, B, C) parameters for init options."""
        # Generate A, B
        A, B = hippo.transition(self.init, self.N)
        A = torch.as_tensor(A, dtype=self.dtype)
        B = torch.as_tensor(B, dtype=self.dtype)[:, 0]
        B = repeat(B, 'n -> v n', v=self.n_ssm).clone().contiguous()
        A = repeat(A, 'n m -> v n m', v=self.n_ssm).clone().contiguous()

        # Generate C
        if self.deterministic:
            C = torch.zeros(self.channels, self.H, self.N, dtype=self.dtype)
            C[..., :1] = 1.0
        else:
            C = torch.randn(self.channels, self.H, self.N, dtype=self.dtype)

        return A, B, C
Returns (dense, real) (A, B, C) parameters for init options.
analyze/convnexts4nd/src/models/sequence/kernels/ssm.py