#include "tensorflow/c/eager/tape.h"
#include "tensorflow/core/common_runtime/eager/attr_builder.h"

namespace tensorflow {
namespace gradients {

// =============== Experimental C++ API for computing gradients ===============

// Sample gradient function:
//
// class AddGradientFunction : public GradientFunction {
//  public:
//   Status Compute(Context* ctx,

  // Looks up the ID of a Gradient.
  int64_t TensorId(AbstractTensorHandle* tensor) const override;

  // Converts a Gradient to a TapeTensor.
  TapeTensor TapeTensorFromGradient(AbstractTensorHandle* g) const override;

  void MarkAsResult(AbstractTensorHandle* gradient) const override;

  void DeleteGradient(AbstractTensorHandle* gradient) const override;

 private:

.table td,.card.bg-primary .bootstrap-datetimepicker-widget .table th{border:none}.card.bg-gradient-primary .bootstrap-datetimepicker-widget table td.day:hover,.card.bg-gradient-primary .bootstrap-datetimepicker-widget table td.hour:hover,.card.bg-gradient-primary .bootstrap-datetimepicker-widget table td.minute:hover,.card.bg-gradient-primary .bootstrap-datetimepicker-widget table td.second:hover,.card.bg-gradient-primary .bootstrap-datetimepicker-widget table thead tr:first-child th:hover,.card.bg-primary...

-primary .bootstrap-datetimepicker-widget table td.second:hover,\n.card.bg-gradient-primary .bootstrap-datetimepicker-widget table thead tr:first-child th:hover,\n.card.bg-gradient-primary .bootstrap-datetimepicker-widget table td.day:hover,\n.card.bg-gradient-primary .bootstrap-datetimepicker-widget table td.hour:hover,\n.card.bg-gradient-primary .bootstrap-datetimepicker-widget table td.minute:hover,\n.card.bg-gradient-primary .bootstrap-datetimepicker-widget table td.second:hover {\n  background-color:...

 * hold the numerical gradient data at the end of the function.
 */
absl::Status CalcNumericalGrad(AbstractContext* ctx, Model forward,
                               absl::Span<AbstractTensorHandle* const> inputs,
                               int input_index, bool use_function,
                               AbstractTensorHandle** numerical_grad);

}  // namespace gradients
}  // namespace tensorflow

  vector<float> theta_data(num_elems);
  memcpy(theta_data.data(), TF_TensorData(theta_tensor),
         TF_TensorByteSize(theta_tensor));

  // Initialize space for the numerical gradient.
  vector<float> dtheta_approx(num_elems);

  // Get theta shape and store in theta_dims.
  int num_dims = TF_NumDims(theta_tensor);
  vector<int64_t> theta_dims(num_dims);

    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
    improve existing gradients such that most ops can now be differentiated
    multiple times.
*   Added a solver for ordinary differential equations,

        // (where the value starts on the host), without allowing other implicit
        // copies/broadcasts. Other implicit copies may be supported eventually,
        // but need special handling for gradients (gradient of copy-on is not
        // just copy-off but includes a sum) and consideration of performance.
        //
        // TODO(allenl): There may be smarter ways to do this copy in some

rgba($color, .25);\n        }\n      }\n    }\n  }\n}\n","// Gradients\n\n@mixin gradient-bg($color) {\n  @if $enable-gradients {\n    background: $color linear-gradient(180deg, mix($body-bg, $color, 15%), $color) repeat-x;\n  } @else {\n    background-color: $color;\n  }\n}\n\n// Horizontal gradient, from left to right\n//\n// Creates two color stops, start and end, by specifying a color and position for each color stop.\n@mixin gradient-x($start-color: $gray-700, $end-color: $gray-800, $start-percent:...

    string expected_msg =
        "No gradient defined for op: TestOpWithNoGradient. Please see "
        "https://www.tensorflow.org/code/"
        "tensorflow/cc/gradients/README.md"
        " for instructions on how to add C++ gradients.";
    EXPECT_EQ(expected_msg, TF_Message(s_));
  }

  // Run the graph and ensure that the gradient values are as expected.