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

#include "tensorflow/c/eager/abstract_context.h"
#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) {

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

 private:
  AbstractContext* parent_ctx_;  // Not owned.
  Tape* tape_;
  const GradientRegistry& registry_;
};
}  // namespace gradients
}  // namespace tensorflow

  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

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

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

namespace tensorflow {
namespace gradients {
TapeOperation::TapeOperation(AbstractOperation* parent_op, Tape* tape,
                             const GradientRegistry& registry)
    : AbstractOperation(kTape),

		`//go:noinline
func {{.Name}}_{{.Type_}}_{{.FNumber}}(a {{.Type_}}) {{.Type_}} { return a {{.Symbol}} {{.Number}} }
`))
	fncCnst2 := template.Must(template.New("fnc").Parse(
		`//go:noinline
func {{.Name}}_{{.FNumber}}_{{.Type_}}(a {{.Type_}}) {{.Type_}} { return {{.Number}} {{.Symbol}} a }
`))

	type fncData struct {
		Name, Type_, Symbol, FNumber, Number string
	}

	for _, s := range szs {

// A TF_ExecutionContext stores knowledge about how to execute an operation.
// E.g. it could know whether we're in eager mode or graph mode, keeps track
// of gradient tapes, etc.
typedef struct TF_ExecutionContext TF_ExecutionContext;

// A TF_AbstractTensor is an input to an operation. E.g. it could be a union
// type of eager and graph tensors. It is also the result of executing an

  AbstractTensorHandle* GetHandle() const;

 private:
  AbstractTensorHandle* handle_;
};

// A tracing/immediate-execution agnostic tape.
//
// Gradient functions defined for this tape must support handling null incoming
// gradients.
class Tape : protected eager::GradientTape<AbstractTensorHandle,
                                           GradientFunction, TapeTensor> {
 public:

    mode: Literal["validation", "serialization"] = "validation",
) -> ModelField:
    """
    Create a new response field. Raises if type_ is invalid.
    """
    class_validators = class_validators or {}
    if PYDANTIC_V2:
        field_info = field_info or FieldInfo(
            annotation=type_, default=default, alias=alias
        )
    else:
        field_info = field_info or FieldInfo()

             absl::Span<AbstractTensorHandle*> outputs) -> Status {
    Tape tape(/*persistent=*/false);
    for (size_t i{}; i < inputs.size(); ++i) {
      tape.Watch(inputs[i]);
    }
    std::vector<AbstractTensorHandle*> temp_outputs(1);
    AbstractContextPtr tape_ctx(new TapeContext(ctx, &tape, grad_registry));
    TF_RETURN_IF_ERROR(

  bool passed() const { return type_ == kSuccess; }

  // Returns true iff the test part failed.
  bool failed() const { return type_ != kSuccess; }

  // Returns true iff the test part non-fatally failed.
  bool nonfatally_failed() const { return type_ == kNonFatalFailure; }

  // Returns true iff the test part fatally failed.
  bool fatally_failed() const { return type_ == kFatalFailure; }

 private: