{* ../../docs_src/security/tutorial005_an_py310.py hl[5,9,13,47,65,106,108:116,122:126,130:136,141,157] *}

Agora vamos revisar essas mudanças passo a passo.

## Esquema de segurança OAuth2 { #oauth2-security-scheme }

A primeira mudança é que agora nós estamos declarando o esquema de segurança OAuth2 com dois escopos disponíveis, `me` e `items`.

{* ../../docs_src/background_tasks/tutorial001_py310.py hl[6:9] *}

## Adicione a tarefa em segundo plano { #add-the-background-task }

Dentro da sua *função de operação de rota*, passe sua função de tarefa para o objeto de *tarefas em segundo plano* com o método `.add_task()`:

{* ../../docs_src/background_tasks/tutorial001_py310.py hl[14] *}

O `.add_task()` recebe como argumentos:

{* ../../docs_src/body_updates/tutorial002_py310.py hl[32] *}

### Using Pydantic's `update` parameter { #using-pydantics-update-parameter }

Now, you can create a copy of the existing model using `.model_copy()`, and pass the `update` parameter with a `dict` containing the data to update.

Like `stored_item_model.model_copy(update=update_data)`:

{* ../../docs_src/body_updates/tutorial002_py310.py hl[33] *}

			err = erasure.DecodeDataBlocks(encoded)
		}

		if err == nil && test.shouldFail {
			t.Errorf("Test %d: test should fail but it passed", i)
		}
		if err != nil && !test.shouldFail {
			t.Errorf("Test %d: test should pass but it failed: %v", i, err)
		}

		decoded := encoded
		if !test.shouldFail {
			if test.reconstructParity {
				for j := range decoded {
					if decoded[j] == nil {

		if err := gopts.SetRange(opts.startOffset, opts.startOffset+opts.length-1); err != nil {
			return nil, s3.ToObjectError(err, object)
		}
	}
	c := &minio.Core{Client: s3.client}
	// Important to use core primitives here to pass range get options as is.
	r, _, _, err := c.GetObject(ctx, s3.Bucket, s3.getDest(object), gopts)
	if err != nil {
		return nil, s3.ToObjectError(err, object)
	}
	return r, nil
}

      ASSERT_EQ(errors::OK, s.code()) << s.message();
      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
    enable_tensor_float_32_execution(false);
  }

  AbstractContextPtr ctx_;

 public:

 * Parameters can be stored as any Object type and retrieved with type conversion
 * support for common types like String.</p>
 *
 * <p>This class is commonly used to pass configuration parameters to data store
 * implementations during crawling operations, allowing for flexible parameter
 * handling without tight coupling to specific parameter schemas.</p>
 */
public class DataStoreParams {

   * interrupting thread to complete the protocol.
   */
  /*
   * This test hangs (or maybe is just *very* slow) under Android.
   *
   * TODO(b/218700094): Ideally, get this to pass under Android. Failing that, convince ourselves
   * that the test isn't exposing a real problem with InterruptibleTask, one that could matter in
   * prod.
   */
  @AndroidIncompatible

   * interrupting thread to complete the protocol.
   */
  /*
   * This test hangs (or maybe is just *very* slow) under Android.
   *
   * TODO(b/218700094): Ideally, get this to pass under Android. Failing that, convince ourselves
   * that the test isn't exposing a real problem with InterruptibleTask, one that could matter in
   * prod.
   */
  @AndroidIncompatible

 * <code>java.lang.CharSequence</code> rather than <code>java.lang.String</code>. This is provided mainly as a
 * convenience, to enable developers to pass things like <code>java.lang.StringBuffer</code> directly into the logger,
 * rather than formatting first by calling <code>toString()</code>.
 *
 * @since 4.0.0
 */
@Experimental
@Provider
public interface Log {