custom estimator with optimizer v2, you have to scale losses. We have
        new utilities to help scale losses `tf.nn.compute_average_loss`,
        `tf.nn.scale_regularization_loss`.

*   `tf.keras`:

    *   Premade models (including Linear and WideDeep) have been introduced for
        the purpose of replacing Premade estimators.
    *   Model saving changes

In fact, in some cases, it will even have **two JSON Schemas** in OpenAPI for the same Pydantic model, for input and output, depending on if they have **default values**.

Let's see how that works and how to change it if you need to do that.

## Pydantic Models for Input and Output

Let's say you have a Pydantic model with default values, like this one:

=== "Python 3.10+"

    * Simple `meshConfig` changes have been approved in the past. Functionality should not be enabled by default.
* For large API changes, 2 members of the TOC must approve the PR before release manager approval in the release branch
  of the istio/api repository. This does not have to wait for the weekly TOC meeting.
    * Risk should be assessed in the PR.
        * Have installs and upgrades affected by this feature?

Por exemplo, extendendo o modelo anterior, você poder decidir por ter uma outra chave `importance` no mesmo corpo, além de `item` e `user`.

Se você declará-lo como é, porque é um valor singular, o **FastAPI** assumirá que se trata de um parâmetro de consulta.

Mas você pode instruir o **FastAPI** para tratá-lo como outra chave do corpo usando `Body`:

=== "Python 3.8+"

    ```Python hl_lines="22"

                "tearDownTwo should have been run before tearDownOne", false, tearDownOne.ran);
          }
        };

    final SimpleTearDown tearDownTwo = new SimpleTearDown(callback);
    stack.addTearDown(tearDownTwo);

    assertEquals(false, tearDownOne.ran);
    assertEquals(false, tearDownTwo.ran);

    stack.runTearDown();

    assertEquals("tearDownOne should have run", true, tearDownOne.ran);

                "tearDownTwo should have been run before tearDownOne", false, tearDownOne.ran);
          }
        };

    final SimpleTearDown tearDownTwo = new SimpleTearDown(callback);
    stack.addTearDown(tearDownTwo);

    assertEquals(false, tearDownOne.ran);
    assertEquals(false, tearDownTwo.ran);

    stack.runTearDown();

    assertEquals("tearDownOne should have run", true, tearDownOne.ran);

<blockquote markdown="1">

**FastAPI** wouldn't exist if not for the previous work of others.

There have been many tools created before that have helped inspire its creation.

I have been avoiding the creation of a new framework for several years. First I tried to solve all the features covered by **FastAPI** using many different frameworks, plug-ins, and tools.

```Python hl_lines="2"
@app.get('/')
def results():
    results = some_library()
    return results
```

---

	// Offset and out size cannot be negative.
	if offset < 0 || length < 0 {
		return 0, errUnexpected
	}

	// Do we have enough blocks?
	if len(enBlocks) < dataBlocks {
		return 0, reedsolomon.ErrTooFewShards
	}

	// Do we have enough data?
	if int64(getDataBlockLen(enBlocks, dataBlocks)) < length {
		return 0, reedsolomon.ErrShortData
	}

	// Counter to decrement total left to write.

	if sepIndex == -1 {
		return nil, fmt.Errorf("'%s' does not have a valid range value", rangeString)
	}

	offsetBeginString := byteRangeString[:sepIndex]
	offsetBegin := int64(-1)
	// Convert offsetBeginString only if its not empty.
	if len(offsetBeginString) > 0 {
		if offsetBeginString[0] == '+' {
			return nil, fmt.Errorf("Byte position ('%s') must not have a sign", offsetBeginString)