// Implementations of the context may contain some state e.g. an execution
// environment, a traced representation etc.
class AbstractContext {
 protected:
  enum AbstractContextKind { kGraph, kMlir, kEager, kTfrt, kTape, kOpHandler };
  explicit AbstractContext(AbstractContextKind kind) : kind_(kind) {}
  virtual ~AbstractContext() {}

 public:
  AbstractContextKind getKind() const { return kind_; }

}

absl::Status MatMulModel(AbstractContext* ctx,
                         absl::Span<AbstractTensorHandle* const> inputs,
                         absl::Span<AbstractTensorHandle*> outputs) {
  return ops::MatMul(ctx, inputs[0], inputs[1], &outputs[0],
                     /*transpose_a=*/false,
                     /*transpose_b=*/false, "MatMul");
}

absl::Status MulModel(AbstractContext* ctx,

// This holds the context for the execution: dispatching operations either to an
// MLIR implementation or to a graph implementation.
class TracingContext : public AbstractContext {
 protected:
  explicit TracingContext(AbstractContextKind kind) : AbstractContext(kind) {}

 public:
  // Add a function parameter and return the corresponding tensor.
  virtual absl::Status AddParameter(DataType dtype,

  for (int i = 0; i < num_dims; i++) {
    out_dims[i] = TF_Dim(t, i);
  }
}

// Runs model as is if output is a scalar,
// else sums the output tensor before returning.
absl::Status RunAndMaybeSum(AbstractContext* ctx, Model forward,
                            absl::Span<AbstractTensorHandle* const> inputs,
                            absl::Span<AbstractTensorHandle*> outputs,
                            bool use_function) {

}

TEST_P(CppGradients, TestSetAttrString) {
  std::unique_ptr<TF_Status, decltype(&TF_DeleteStatus)> status(
      TF_NewStatus(), TF_DeleteStatus);
  AbstractContextPtr ctx;
  {
    AbstractContext* ctx_raw = nullptr;
    absl::Status s =
        BuildImmediateExecutionContext(std::get<1>(GetParam()), &ctx_raw);
    ASSERT_EQ(errors::OK, s.code()) << s.message();
    ctx.reset(ctx_raw);
  }

// Public C API entry points
// These are only the entry points specific to the Eager API.
// =============================================================================

using tensorflow::AbstractContext;
using tensorflow::AbstractTensorHandle;
using tensorflow::dyn_cast;
using tensorflow::ImmediateExecutionContext;
using tensorflow::ImmediateExecutionTensorHandle;
using tensorflow::string;
using tensorflow::unwrap;

// }
//
// Status RegisterGradients(GradientRegistry* registry) {
//   return registry->Register("Add", AddRegisterer);
// }
class GradientFunction {
 public:
  virtual absl::Status Compute(
      AbstractContext* ctx,
      absl::Span<AbstractTensorHandle* const> grad_outputs,
      absl::Span<AbstractTensorHandle*> grad_inputs) = 0;
  virtual ~GradientFunction() {}
};

                                 absl::Span<const AbstractOperation*> values,
                                 ForwardOperation*);

// Make the call to `Tape::RecordOperation`.
absl::Status Execute(AbstractOperation*, AbstractContext*,
                     absl::Span<AbstractTensorHandle*> retvals,
                     int* num_retvals, ForwardOperation*, Tape*,
                     const GradientRegistry&);

}  // namespace internal

 *
 * `numerical_grad` is the pointer to the AbstractTensorHandle* which will
 * 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,