///

/// 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 | "情報"

<a href="https://docs.python.org/3/library/enum.html" class="external-link" target="_blank">Enumerations (もしくは、enums)はPython 3.4以降で利用できます</a>。

///

/// tip | "豆知識"

"AlexNet"、"ResNet"そして"LeNet"は機械学習<abbr title="Technically, Deep Learning model architectures">モデル</abbr>の名前です。

///

### *パスパラメータ*の宣言

次に、作成したenumクラスである`ModelName`を使用した型アノテーションをもつ*パスパラメータ*を作成します:

```Python hl_lines="16"

    assertEquals("9.999 \u03bcs", stopwatch.toString());
    stopwatch.reset();
    stopwatch.start();
    ticker.advance(1234567);
    assertEquals("1.235 ms", stopwatch.toString());
    stopwatch.reset();
    stopwatch.start();
    ticker.advance(5000000000L);
    assertEquals("5.000 s", stopwatch.toString());
    stopwatch.reset();
    stopwatch.start();
    ticker.advance((long) (1.5 * 60 * 1000000000L));

    assertTrue(reader.markSupported());

    assertEquals(string, readFully(reader));
    assertFullyRead(reader);

    // reset and read again
    reader.reset();
    assertEquals(string, readFully(reader));
    assertFullyRead(reader);

    // reset, skip, mark, then read the rest
    reader.reset();
    assertEquals(5, reader.skip(5));
    reader.mark(Integer.MAX_VALUE);
    assertEquals(string.substring(5), readFully(reader));

    A.reset(A_raw);
  }
  float B_vals[] = {.5f, -1.0f, 1.0f, 1.0f};
  int64_t B_dims[] = {2, 2};
  AbstractTensorHandlePtr B;
  {
    AbstractTensorHandle* B_raw;
    absl::Status s = TestTensorHandleWithDims<float, TF_FLOAT>(
        ctx_.get(), B_vals, B_dims, 2, &B_raw);
    ASSERT_EQ(errors::OK, s.code()) << s.message();
    B.reset(B_raw);
  }

    assertEquals("9.999 \u03bcs", stopwatch.toString());
    stopwatch.reset();
    stopwatch.start();
    ticker.advance(1234567);
    assertEquals("1.235 ms", stopwatch.toString());
    stopwatch.reset();
    stopwatch.start();
    ticker.advance(5000000000L);
    assertEquals("5.000 s", stopwatch.toString());
    stopwatch.reset();
    stopwatch.start();
    ticker.advance((long) (1.5 * 60 * 1000000000L));

	)

	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

    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(

		rcfg:         replicationConfig{Config: &configs[0]},
		expectedSync: false,
	},
	{ // 4. existing object replication enabled, versioning enabled; no reset in progress
		name: "existing object replication enabled, versioning enabled; no reset in progress",
		info: ObjectInfo{
			Size:              100,
			ReplicationStatus: replication.Completed,
			VersionID:         "a3348c34-c352-4498-82f0-1098e8b34df9",
		},

    counter.mark(5);
    counter.read();
    assertEquals(11, counter.getCount());
    counter.reset();
    assertEquals(10, counter.getCount());
    assertEquals(10, counter.skip(100));
    assertEquals(20, counter.getCount());
  }

  public void testMarkNotSet() {
    IOException expected = assertThrows(IOException.class, () -> counter.reset());
    assertThat(expected).hasMessageThat().isEqualTo("Mark not set");
  }