==============================================================================*/
#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 {

  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:

  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:

  Tape tape(/*persistent=*/false);
  tape.Watch(inputs[0]);  // Watch x.
  AbstractTensorHandle* exp_output;
  TF_RETURN_IF_ERROR(ops::Exp(ctx, inputs[0], &exp_output, "Exp"));
  std::unique_ptr<GradientFunction> gradient_function(
      new PassThroughGradientFunction);
  tape.RecordOperation(inputs, {exp_output}, gradient_function.release());
  TF_RETURN_IF_ERROR(tape.ComputeGradient(ctx,

    absl::Span<AbstractTensorHandle*> outputs) {
  Tape tape(/*persistent=*/false);
  tape.Watch(inputs[0]);
  AbstractTensorHandle* neg_output;
  TF_RETURN_IF_ERROR(ops::Neg(ctx, inputs[0], &neg_output, "Neg"));
  tape.RecordOperation(inputs, {neg_output}, nullptr, "Neg");
  return tape.ComputeGradient(ctx,
                              /*targets=*/{neg_output},

# A tape built on top of unified execution APIs.
load("//tensorflow/core/platform:rules_cc.bzl", "cc_library")

package(
    # copybara:uncomment default_applicable_licenses = ["//tensorflow:license"],
    licenses = ["notice"],
)

cc_library(
    name = "tape_context",
    srcs = ["tape_context.cc"],
    hdrs = [
        "tape_context.h",
    ],
    visibility = [
        "//tensorflow:internal",
    ],
    deps = [