// the state of the ForwardOperation and call the tape as appropriate.
// These APIs are mainly to facilitate testing and are subject to change.

// Records the op name in the `ForwardOperation`.
Status Reset(AbstractOperation*, const char* op, const char* raw_device_name,
             ForwardOperation*);

// Records the inputs in the `ForwardOperation`.

namespace gradients {

GradientFunction* AddRegisterer(const ForwardOperation& op);
GradientFunction* ExpRegisterer(const ForwardOperation& op);
GradientFunction* MatMulRegisterer(const ForwardOperation& op);
GradientFunction* SqrtRegisterer(const ForwardOperation& op);
GradientFunction* NegRegisterer(const ForwardOperation& op);
GradientFunction* SubRegisterer(const ForwardOperation& op);
GradientFunction* MulRegisterer(const ForwardOperation& op);

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

namespace tensorflow {
namespace gradients {
GradientFunction* ReluRegisterer(const ForwardOperation& op);
GradientFunction* SparseSoftmaxCrossEntropyWithLogitsRegisterer(
    const ForwardOperation& op);
GradientFunction* BiasAddRegisterer(const ForwardOperation& op);
}  // namespace gradients
}  // namespace tensorflow

}  // namespace

GradientFunction* ReluRegisterer(const ForwardOperation& op) {
  return new ReluGradientFunction(op.outputs);
}

GradientFunction* SparseSoftmaxCrossEntropyWithLogitsRegisterer(
    const ForwardOperation& op) {
  return new SparseSoftmaxCrossEntropyWithLogitsGradientFunction(op.outputs);
}

GradientFunction* BiasAddRegisterer(const ForwardOperation& op) {
  return new BiasAddGradientFunction(op.attrs);
}

// gradient registerer to instantiate a GradientFunction.
struct ForwardOperation {
 public:
  string op_name;
  std::vector<AbstractTensorHandle*> inputs;
  std::vector<AbstractTensorHandle*> outputs;
  std::vector<int64_t> skip_input_indices;
  AttrBuilder attrs;
};

using GradientFunctionFactory =
    std::function<GradientFunction*(const ForwardOperation& op)>;

// Map from op name to a `GradientFunctionFactory`.

    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

#define TENSORFLOW_C_EXPERIMENTAL_GRADIENTS_ARRAY_GRAD_H_

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

namespace tensorflow {
namespace gradients {
GradientFunction* IdentityNRegisterer(const ForwardOperation& op);
}  // namespace gradients
}  // namespace tensorflow

      }
      grad_inputs[i] = grad_input;
    }
    return absl::OkStatus();
  }
  ~IdentityNGradientFunction() override {}
};
}  // namespace

GradientFunction* IdentityNRegisterer(const ForwardOperation& op) {
  return new IdentityNGradientFunction;
}

}  // namespace gradients

  // For LLVM style RTTI.
  static bool classof(const AbstractOperation* ptr) {
    return ptr->getKind() == kTape;
  }
  ~TapeOperation() override;

 private:
  AbstractOperation* parent_op_;
  ForwardOperation forward_op_;
  Tape* tape_;
  const GradientRegistry& registry_;
};

}  // namespace gradients
}  // namespace tensorflow

    ASSERT_EQ(errors::OK, s.code()) << s.message();
    t.reset(x_raw);
  }

  AbstractOperationPtr check_numerics_op(ctx->CreateOperation());
  ForwardOperation forward_op;
  Status s = Reset(check_numerics_op.get(), "CheckNumerics",
                   /*raw_device_name=*/nullptr, &forward_op);
  ASSERT_EQ(errors::OK, s.code()) << s.message();