* Fornecer diretamente 2 interfaces web de documentação interativa.

---

Nós apenas arranhamos a superfície, mas você já tem ideia de como tudo funciona.

Experimente mudar a seguinte linha:

```Python
    return {"item_name": item.name, "item_id": item_id}
```

...de:

```Python
        ... "item_name": item.name ...
```

...para:

/// info | Informação

Esta página lhe ensinará como usar **ambientes virtuais** e como eles funcionam.

Se você estiver pronto para adotar uma **ferramenta que gerencia tudo** para você (incluindo a instalação do Python), experimente [uv](https://github.com/astral-sh/uv).

///

## Criar um Projeto { #create-a-project }

Primeiro, crie um diretório para seu projeto.

        endpoint.
    *   Promoting `tf.data.experimental.RandomDataset` API to
        `tf.data.Dataset.random` and deprecating the experimental endpoint.
    *   Promoting `tf.data.experimental.scan` API to `tf.data.Dataset.scan` and
        deprecating the experimental endpoint.
    *   Promoting `tf.data.experimental.snapshot` API to
        `tf.data.Dataset.shapshot` and deprecating the experimental endpoint.

Experimental properties MUST NOT be supplemented with command-line options.
They can always be passed via `-D` on the command line.
The feature should leave the experimental stage to receive a build option.

The difference between internal properties and experimental properties is the amount of publicity they are intended to receive.
If the intention is to gather user feedback on an early prototype, then the experimental property should be used.

    PER_DAY(
        displayName = "Slow Performance Regression Test",
        timeout = 420,
        channel = "commits",
    ),
    PER_WEEK(
        displayName = "Performance Experiment",
        timeout = 420,
        channel = "experiments",
    ),
    FLAKINESS_DETECTION(
        displayName = "Performance Test Flakiness Detection",
        timeout = 600,
        defaultBaselines = "flakiness-detection-commit",

        "//tensorflow/c:tf_status_helper",
        "//tensorflow/c/experimental/gradients:array_grad",
        "//tensorflow/c/experimental/gradients:math_grad",
        "//tensorflow/c/experimental/gradients:not_differentiable",
        "//tensorflow/c/experimental/gradients/tape:tape_context",
        "//tensorflow/c/experimental/ops",
        "//tensorflow/compiler/mlir/tensorflow/c:mlir_c_api_registration",

    ] + select({
        "//tensorflow:android": [
            "//tensorflow/c/experimental/stream_executor:stream_executor_hdrs",
            "//tensorflow/core:portable_tensorflow_lib_lite",
        ],
        "//conditions:default": [
            ":tf_tensor",
            "//tensorflow/c/experimental/stream_executor",
            "//tensorflow/c/experimental/stream_executor:stream_executor_internal",
            "//tensorflow/core:framework",

 * under the License.
 */
package org.apache.maven.api.services;

import org.apache.maven.api.annotations.Consumer;
import org.apache.maven.api.annotations.Experimental;

/**
 * @since 4.0.0
 */
@Experimental
@Consumer
public class TransportProviderException extends MavenException {

    public TransportProviderException(String message, Throwable cause) {
        super(message, cause);
    }

 * under the License.
 */
package org.apache.maven.api.services;

import org.apache.maven.api.annotations.Experimental;

/**
 * The Exception class throw by the {@link ToolchainsBuilder}.
 *
 * @since 4.0.0
 */
@Experimental
public class ToolchainsBuilderException extends MavenBuilderException {

    /**
     * @param message the message to give

 * specific language governing permissions and limitations
 * under the License.
 */
package org.apache.maven.api.services;

import org.apache.maven.api.annotations.Experimental;

/**
 *
 *
 * @since 4.0.0
 */
@Experimental
public class ArtifactResolverException extends MavenException {

    private final ArtifactResolverResult result;

    /**
     * @param message the message for the exception