<a href="https://docs.python.org/3/library/enum.html" class="external-link" target="_blank">Enumerations (ou enums) estão disponíveis no Python</a> desde a versão 3.4.

!!! tip "Dica"
	Se você está se perguntando, "AlexNet", "ResNet", e "LeNet" são apenas nomes de <abbr title="técnicamente, modelos de arquitetura de Deep Learning">modelos</abbr> de Machine Learning (aprendizado de máquina).

### Declare um *parâmetro de rota*

```

!!! info "정보"
    <a href="https://docs.python.org/3/library/enum.html" class="external-link" target="_blank">열거형(또는 enums)</a>은 파이썬 버전 3.4 이후로 사용 가능합니다.

!!! tip "팁"
    혹시 궁금하다면, "AlexNet", "ResNet", 그리고 "LeNet"은 그저 기계 학습 <abbr title="기술적으로 정확히는 딥 러닝 모델 구조">모델</abbr>들의 이름입니다.

### *경로 매개변수* 선언

생성한 열거형 클래스(`ModelName`)를 사용하는 타입 어노테이션으로 *경로 매개변수*를 만듭니다:

```Python hl_lines="16"

	)

	if config.Colorful {
		infoStr = Green + "%s\n" + Reset + Green + "[info] " + Reset
		warnStr = BlueBold + "%s\n" + Reset + Magenta + "[warn] " + Reset
		errStr = Magenta + "%s\n" + Reset + Red + "[error] " + Reset
		traceStr = Green + "%s\n" + Reset + Yellow + "[%.3fms] " + BlueBold + "[rows:%v]" + Reset + " %s"
		traceWarnStr = Green + "%s " + Yellow + "%s\n" + Reset + RedBold + "[%.3fms] " + Yellow + "[rows:%v]" + Magenta + " %s" + Reset

	type args struct {
		ctrzClient      *ControlzClient
		reset           bool
		outputLogLevel  string
		stackTraceLevel string
		outputFormat    string
	}
	tests := []struct {
		name string
		args args
		want *istiodConfigLog
	}{
		{
			name: "given --reset flag return reset command",
			args: args{
				ctrzClient:      ctrzClient,
				reset:           true,
				outputLogLevel:  "",

    var_to_data_type_map_.swap(result.second);
  } else {
    reader_.reset(new TensorSliceReader(filename));
    if (!reader_->status().ok()) {
      tsl::Set_TF_Status_from_Status(status, reader_->status());
      return;
    }
    var_to_shape_map_.reset(
        new TensorSliceReader::VarToShapeMap(reader_->GetVariableToShapeMap()));
    var_to_data_type_map_.reset(new TensorSliceReader::VarToDataTypeMap(

      status_ =
          BuildImmediateExecutionContext(std::get<1>(GetParam()), &ctx_raw);
      ASSERT_EQ(errors::OK, status_.code()) << status_.message();
      immediate_execution_ctx_.reset(ctx_raw);
    }

    // Computing numerical gradients with TensorFloat-32 is numerically
    // unstable. Some forward pass tests also fail with TensorFloat-32 due to
    // low tolerances

        has been updated.
    *   Object metadata has now been deprecated and no longer saved to the
        SavedModel.

*   TF Core:

    *   Added `tf.config.experimental.reset_memory_stats` to reset the tracked
        peak memory returned by `tf.config.experimental.get_memory_info`.

*   `tf.data`:

    *   Added `target_workers` param to `data_service_ops.from_dataset_id` and

      }
    }
    out.close();

    // Check that source returns the right data
    assertTrue(Arrays.equals(data, source.read()));

    // Make sure that reset deleted the file
    out.reset();
    if (file != null) {
      assertFalse(file.exists());
    }
  }


  public void testThreshold_resetOnFinalize() throws Exception {
    testThreshold(0, 100, true, true);

    ASSERT_EQ(errors::OK, status_.code()) << status_.message();
    x.reset(x_raw);
  }

  AbstractTensorHandlePtr y;
  {
    AbstractTensorHandle* y_raw = nullptr;
    status_ = TestScalarTensorHandle<float, TF_FLOAT>(
        immediate_execution_ctx_.get(), 2.0f, &y_raw);
    ASSERT_EQ(errors::OK, status_.code()) << status_.message();
    y.reset(y_raw);
  }

	f, err := os.Open(path)
	if err != nil {
		return nil, err
	}

	bf := bytes.Buffer{}

	save := func(s string) {
		bf.WriteString(s + "\n")
	}
	reset := func() {
		bf.Reset()
	}

	ret := make(map[time.Duration][]string)

	s := bufio.NewScanner(f)
	s.Split(bufio.ScanLines)

	var (
		t            time.Duration
		skip, record bool
	)

	for s.Scan() {