* @return the converted instance; <b>must not</b> be null
   * @throws UnsupportedOperationException if backward conversion is not implemented; this should be
   *     very rare. Note that if backward conversion is not only unimplemented but
   *     unimplement<i>able</i> (for example, consider a {@code Converter<Chicken, ChickenNugget>}),
   *     then this is not logically a {@code Converter} at all, and should just implement {@link
   *     Function}.

   * @return the converted instance; <b>must not</b> be null
   * @throws UnsupportedOperationException if backward conversion is not implemented; this should be
   *     very rare. Note that if backward conversion is not only unimplemented but
   *     unimplement<i>able</i> (for example, consider a {@code Converter<Chicken, ChickenNugget>}),
   *     then this is not logically a {@code Converter} at all, and should just implement {@link
   *     Function}.

	if haveHighResSleep {
		return delay
	}
	t.Log("adjusting delay for low resolution sleep")
	switch runtime.GOOS {
	case "windows":
		return delay - 17*Millisecond
	default:
		t.Fatal("adjustDelay unimplemented on " + runtime.GOOS)
		return 0
	}
}

func TestSleep(t *testing.T) {
	const delay = 100 * Millisecond
	go func() {
		Sleep(delay / 2)
		Interrupt()
	}()
	start := Now()
	Sleep(delay)

	MOVL	16(DI), DX	// arg 3 advice
	MOVQ	0(DI), DI	// arg 1 addr
	CALL	libc_madvise(SB)
	// ignore failure - maybe pages are locked
	RET

TEXT runtime·mlock_trampoline(SB), NOSPLIT, $0
	UNDEF // unimplemented

GLOBL timebase<>(SB),NOPTR,$(machTimebaseInfo__size)

TEXT runtime·nanotime_trampoline(SB),NOSPLIT,$0
	MOVQ	DI, BX
	CALL	libc_mach_absolute_time(SB)
	MOVQ	AX, 0(BX)

    TF_RETURN_IF_ERROR(stream->RecordEvent(definition_event.get()));
  }

  for (const XlaOutputDescription& descr : compilation_result->outputs) {
    if (descr.type == DT_VARIANT) {
      return errors::Unimplemented(
          "Support for TensorList crossing the XLA/TF boundary "
          "is not implemented");
    }
  }

  std::vector<TensorShape> output_tensor_shapes;

			kind:                p.kind,
			userAgent:           p.userAgent,
			validationDirective: p.validationDirective,
		}
		p.forceAllowCreate = true
	default:
		return nil, false, fmt.Errorf("%v: unimplemented patch type", p.patchType)
	}
	dedupOwnerReferencesTransformer := func(_ context.Context, obj, _ runtime.Object) (runtime.Object, error) {
		// Dedup owner references after mutating admission happens

      *str = "tensorflow::uint64";
      break;
    case xla::F32:
      *str = "float";
      break;
    case xla::F64:
      *str = "double";
      break;
    default:
      return errors::Unimplemented("XLA type ", xla::PrimitiveType_Name(type),
                                   " has no equivalent in C++");
  }
  return absl::OkStatus();
}

// Returns the sum of the size of each buffer in `buffer_infos`.

}

template <GTEST_10_TYPENAMES_(T), GTEST_10_TYPENAMES_(U)>
inline bool operator!=(const GTEST_10_TUPLE_(T)& t,
                       const GTEST_10_TUPLE_(U)& u) { return !(t == u); }

// 6.1.4 Pairs.
// Unimplemented.

}  // namespace tr1
}  // namespace std

#undef GTEST_0_TUPLE_
#undef GTEST_1_TUPLE_
#undef GTEST_2_TUPLE_
#undef GTEST_3_TUPLE_
#undef GTEST_4_TUPLE_
#undef GTEST_5_TUPLE_

type FormatError string

func (e FormatError) Error() string { return "invalid JPEG format: " + string(e) }

// An UnsupportedError reports that the input uses a valid but unimplemented JPEG feature.
type UnsupportedError string

func (e UnsupportedError) Error() string { return "unsupported JPEG feature: " + string(e) }

  void (*delete_device)(void* device_info);

  // Implements TFE_CreatePackedTensorHandle when one of `handles` is on this
  // custom device.
  //
  // Many devices will want to simply return an "unimplemented" status
  // here. This is the default behavior if `pack` is null when passed to
  // TFE_RegisterCustomDevice.
  TFE_TensorHandle* (*pack)(TFE_Context* context, TFE_TensorHandle** handles,