void CheckTensorValue(AbstractTensorHandle* t, absl::Span<const float> manuals,
                      absl::Span<const int64_t> dims, double abs_error = 1e-2);

Model BuildGradModel(Model forward, GradientRegistry registry);

}  // namespace internal
}  // namespace gradients
}  // namespace tensorflow

    absl::Span<AbstractTensorHandle*> grad_inputs) {
  for (int i = 0; i < grad_inputs.size(); i++) {
    grad_inputs[i] = nullptr;
  }
  return OkStatus();
}

Status RegisterNotDifferentiable(GradientRegistry* registry, const string& op) {
  return registry->Register(op, [](const ForwardOperation& op) {
    return new NotDifferentiableGradientFunction;
  });
}
}  // namespace gradients

#include "tensorflow/c/experimental/gradients/tape/tape_operation.h"

namespace tensorflow {
namespace gradients {
class TapeContext : public AbstractContext {
 public:
  explicit TapeContext(AbstractContext*, Tape*, const GradientRegistry&);
  void Release() override;
  TapeOperation* CreateOperation() override;
  Status RegisterFunction(AbstractFunction*) override;
  Status RemoveFunction(const string& func) override;
  // For LLVM style RTTI.

#include "tensorflow/c/eager/gradients.h"

namespace tensorflow {
namespace gradients {
class TapeOperation : public AbstractOperation {
 public:
  explicit TapeOperation(AbstractOperation*, Tape*, const GradientRegistry&);
  void Release() override;
  Status Reset(const char* op, const char* raw_device_name) override;
  const string& Name() const override;
  const string& DeviceName() const override;

#include "tensorflow/c/eager/gradients.h"

namespace tensorflow {
namespace gradients {
TapeOperation::TapeOperation(AbstractOperation* parent_op, Tape* tape,
                             const GradientRegistry& registry)
    : AbstractOperation(kTape),
      parent_op_(parent_op),
      tape_(tape),
      registry_(registry) {
  // TODO(b/172003047): Consider making AbstractOperation RefCounted.
  // parent_op_->Ref();

    // unstable. Some forward pass tests also fail with TensorFloat-32 due to
    // low tolerances
    enable_tensor_float_32_execution(false);
  }

  AbstractContextPtr immediate_execution_ctx_;
  GradientRegistry registry_;
  Status status_;

 public:
  bool UseMlir() const { return strcmp(std::get<0>(GetParam()), "mlir") == 0; }
  bool UseFunction() const { return std::get<2>(GetParam()); }
};

#include "tensorflow/c/experimental/gradients/tape/tape_operation.h"

namespace tensorflow {
namespace gradients {
TapeContext::TapeContext(AbstractContext* c, Tape* tape,
                         const GradientRegistry& registry)
    : AbstractContext(kTape), parent_ctx_(c), tape_(tape), registry_(registry) {
  // TODO(srbs): Make AbstractContext ref counted.
  // parent_ctx_->Ref();
}
void TapeContext::Release() {

//   // More complex gradient functions can use inputs/attrs etc. from the
//   // forward `op`.
//   return new AddGradientFunction;
// }
//
// Status RegisterGradients(GradientRegistry* registry) {
//   return registry->Register("Add", AddRegisterer);
// }
class GradientFunction {
 public:
  virtual Status Compute(AbstractContext* ctx,

  TF_RETURN_IF_ERROR(
      op->Execute(absl::Span<AbstractTensorHandle*>(outputs), &num_outputs));
  *result = outputs[0];
  return absl::OkStatus();
}
}  // namespace

Status GradientRegistry::Register(
    const string& op_name, GradientFunctionFactory gradient_function_factory) {
  auto iter = registry_.find(op_name);
  if (iter != registry_.end()) {

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

}  // namespace internal
}  // namespace gradients
}  // namespace tensorflow