*max_error = 0.0;
    auto jac_t = jacobian_ts[i].matrix<JAC_T>();
    auto jac_n = jacobian_ns[i].matrix<JAC_T>();
    for (int r = 0; r < jacobian_ts[i].dim_size(0); ++r) {
      for (int c = 0; c < jacobian_ts[i].dim_size(1); ++c) {
        auto cur_error = std::fabs(jac_t(r, c) - jac_n(r, c));
        // Treat any NaN as max_error and immediately return.
        // (Note that std::max may ignore NaN arguments.)

/// <X_T, Y_T, JAC_T> should be <complex64, complex64, float>
/// Note that JAC_T is always real-valued, and should be an appropriate
/// precision to host the partial derivatives for dy/dx
///
/// if y = ComplexAbs(x) where x is DT_COMPLEX64 (so y is DT_FLOAT)
/// <X_T, Y_T, JAC_T> should be <complex64, float, float>
///
/// if y = Complex(x, x) where x is DT_FLOAT (so y is DT_COMPLEX64)

    MakeOp<X_T>(op_type, x_data, {2, 3}, align_corners, half_pixel_centers, &x,
                &y);
    JAC_T max_error;
    TF_ASSERT_OK((ComputeGradientError<X_T, Y_T, JAC_T>(
        scope_, x, x_data, y, {1, 2, 3, 1}, &max_error)));
    EXPECT_LT(max_error, 1.5e-3);
  }

  template <typename X_T, typename Y_T, typename JAC_T>
  void TestResizeToLargerAndAlign(const OpType op_type,