Input{100.0f, 200.0f}};
  Scope scope_;
};

TEST_F(ScaleAndTranslateGradTest, TestGrads) {
  const std::vector<std::string> kKernelTypes = {"lanczos1", "lanczos3",
                                                 "lanczos5", "gaussian"};
  constexpr int kOutHeight = 4;
  constexpr int kOutWidth = 6;

  const TensorShape kXShape = TensorShape({1, 2, 3, 1});
  for (const Input scale : kScales) {

    rewriter.replaceOpWithNewOp<TensorScatterUpdateOp>(
        op, result_type, op.getX(), indices, updates);
    return success();
  }
};

// Approximates lgamma using Lanczos' approximation from
// "A Precision Approximation of the Gamma Function". SIAM Journal on Numerical
// Analysis series B. Vol. 1:
// lgamma(z + 1) = (log(2) + log(pi)) / 2 + (z + 1/2) * log(t(z)) - t(z) + A(z)

*   Added a new library for library of matrix-free (iterative) solvers for
    linear equations, linear least-squares, eigenvalues and singular values in
    tensorflow/contrib/solvers. Initial version has lanczos bidiagonalization,
    conjugate gradients and CGLS.
*   Added gradients for `matrix_solve_ls` and `self_adjoint_eig`.
*   Large cleanup to add second order gradient for ops with C++ gradients and