def METHOD_NAME(self):
    img_shape = [16, 16]
    mps_shape = [8, 16, 16]
    img = sp.randn(img_shape, dtype=complex)
    mps = sp.randn(mps_shape, dtype=complex)
    y, x = np.mgrid[:16, :16]
    coord = np.stack([np.ravel(y - 8), np.ravel(x - 8)], axis=1)
    coord = coord.astype(float)
    d = np.sqrt(x * x + y * y)
    sigma, mu, a = 2, 0.25, 400
    b0 = a * np.exp(-((d - mu) ** 2 / (2.0 * sigma**2)))
    tseg = {"b0": b0, "dt": 4e-6, "lseg": 1, "n_bins": 10}
    F = sp.linop.NUFFT(mps_shape, coord)
    b, ct = sp.mri.util.tseg_off_res_b_ct(
        b0=b0, bins=10, lseg=1, dt=4e-6, T=coord.shape[0] * 4e-6
    )
    B1 = sp.linop.Multiply(F.oshape, b.T)
    Ct1 = sp.linop.Multiply(img_shape, ct.reshape(img_shape))
    S = sp.linop.Multiply(img_shape, mps)
    A = linop.Sense(mps, coord=coord, tseg=tseg)
    check_linop_adjoint(A, dtype=complex)
    npt.assert_allclose(B1 * F * S * Ct1 * img, A * img)