Em seguida, crie atributos de classe com valores fixos, que serão os valores válidos disponíveis:

{* ../../docs_src/path_params/tutorial005_py310.py hl[1,6:9] *}

/// tip | Dica
Se você está se perguntando, "AlexNet", "ResNet" e "LeNet" são apenas nomes de modelos de Aprendizado de Máquina <dfn title="Tecnicamente, arquiteturas de modelos de Deep Learning">modelos</dfn>.
///

### Declare um parâmetro de path { #declare-a-path-parameter }

Після цього створіть атрибути класу з фіксованими значеннями, які будуть доступними коректними значеннями:

{* ../../docs_src/path_params/tutorial005_py310.py hl[1,6:9] *}

/// tip | Порада

Якщо вам цікаво, «AlexNet», «ResNet» та «LeNet» — це просто назви моделей машинного навчання <dfn title="Технічно, архітектури моделей глибокого навчання">моделі</dfn>.

///

### Оголосіть *параметр шляху* { #declare-a-path-parameter }

{* ../../docs_src/path_params/tutorial005_py310.py hl[1,6:9] *}

/// tip | Astuce

Si vous vous demandez, « AlexNet », « ResNet » et « LeNet » sont juste des noms de <dfn title="Techniquement, architectures de modèles de Deep Learning">modèles</dfn> de Machine Learning.

///

### Déclarer un paramètre de chemin { #declare-a-path-parameter }

        This class implements the sharpness-aware minimization technique, which
        boosts model performance on various tasks, e.g., ResNet on image
        classification.

*   `tf.data`:

    *   Added support for cross-trainer data caching in tf.data service. This
        saves computation resources when concurrent training jobs train from the

                    throw new SmbException("Account '" + username + "' is locked out until " + account.getLockoutExpiry());
                } else {
                    // Lockout expired, reset
                    account.reset();
                }
            }
        }

        // Check per-IP limit
        if (sourceIp != null) {

        assertEquals(State.OPEN, circuitBreaker.getState(), "Circuit should be OPEN");

        // Manually reset
        circuitBreaker.reset();
        assertEquals(State.CLOSED, circuitBreaker.getState(), "Circuit should be CLOSED after reset");
        assertEquals(0, circuitBreaker.getFailureCount(), "Failure count should be reset");
    }

    @Test
    public void testCustomFailureDetection() throws CIFSException {

    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(

            HMACT64 hmac = new HMACT64(TEST_KEY);
            hmac.engineReset();
            verify(mockMd5, times(2)).reset(); // Once in constructor, once in reset
            verify(mockMd5, times(2)).update(any(byte[].class)); // Once in constructor, once in reset
        }
    }

    @Test
    void testEngineGetDigestLength() throws NoSuchAlgorithmException {
        // Test engineGetDigestLength()

	)

	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

        buf.reset();
        ns.encode(buf); // should not throw

        // After encoding, check how many bytes were used
        int bytesUsed = buf.getIndex();
        // Should be 2 bytes for the short value (alignment may add padding)
        assertTrue(bytesUsed >= 2, "Should use at least 2 bytes for short");

        // Reset index to read back
        buf.reset();