<li><a href="http://cs/f:devtools/kokoro/config/prod/$KOKORO_JOB_NAME">Codesearch - job definition</a></li>
<li><a href="http://cs/f:learning/brain/testing/kokoro/$(echo "$KOKORO_JOB_NAME" | sed 's!tensorflow/!!g')">Codesearch - build definition & scripts</a></li>
<li><a href="http://cs/$KOKORO_JOB_NAME">Codesearch - All references to this job</a></li>
</ul>

    * If you know Python types you know how to use Pydantic.
* Plays nicely with your **<abbr title="Integrated Development Environment, similar to a code editor">IDE</abbr>/<abbr title="A program that checks for code errors">linter</abbr>/brain**:
    * Because pydantic data structures are just instances of classes you define; auto-completion, linting, mypy and your intuition should all work properly with your validated data.
* Validate **complex structures**:

* **更简单**：
    * 没有新的模式定义 micro-language 需要学习。
    * 如果你知道 Python types，你就知道如何使用 Pydantic。
* 和你 **<abbr title="集成开发环境，和代码编辑器类似">IDE</abbr>/<abbr title="一个检查代码错误的程序">linter</abbr>/brain** 适配:
    * 因为 pydantic 数据结构仅仅是你定义的类的实例；自动补全，linting，mypy 以及你的直觉应该可以和你验证的数据一起正常工作。
* 验证**复杂结构**:
    * 使用分层的 Pydantic 模型, Python `typing`的 `List` 和 `Dict` 等等。
    * 验证器使我们能够简单清楚的将复杂的数据模式定义、检查并记录为 JSON Schema。

        if (result.isOverflow()) {
          // Not enough room in output buffer--drain it, creating a bigger buffer if necessary.
          startDraining(true);
          continue DRAINING;
        } else if (result.isUnderflow()) {
          // If encoder underflows, it means either:
          // a) the final flush() succeeded; next drain (then done)
          // b) we encoded all of the input; next flush

   * networks. Example:
   *
   * <pre>{@code
   * static final ImmutableNetwork<City, Train> TRAIN_NETWORK =
   *     NetworkBuilder.undirected()
   *         .allowsParallelEdges(true)
   *         .<City, Train>immutable()
   *         .addEdge(PARIS, BRUSSELS, Thalys.trainNumber("1111"))
   *         .addEdge(PARIS, BRUSSELS, RegionalTrain.trainNumber("2222"))

package http

import (
	"io"

	xioutil "github.com/minio/minio/internal/ioutil"
)

// DrainBody close non nil response with any response Body.
// convenient wrapper to drain any remaining data on response body.
//
// Subsequently this allows golang http RoundTripper
// to reuse the same connection for future requests.
func DrainBody(respBody io.ReadCloser) {

from anywhere, and executes the graphs it is sent without performing any checks.
Therefore, if you run a `tf.train.Server` in your network, anybody with access
to the network can execute arbitrary code with the privileges of the user
running the `tf.train.Server`.

## Untrusted inputs during training and prediction

TensorFlow supports a wide range of input data formats. For example it can

		result := drain(input)
		if result != test.output {
			t.Errorf("%s: got %q expected %q", test.name, result, test.output)
		}
	}
}

// lines joins the arguments together as complete lines.
func lines(a ...string) string {
	return strings.Join(a, "\n") + "\n"
}

// drain returns a single string representing the processed input tokens.

    defaultConfig:
      proxyMetadata: {}

  # This value defines:
  # 1. how many seconds kube waits for ztunnel pod to gracefully exit before forcibly terminating it (this value)
  # 2. how many seconds ztunnel waits to drain its own connections (this value - 1 sec)
  # Default K8S value is 30 seconds
  terminationGracePeriodSeconds: 30

  # Revision is set as 'version' label and part of the resource names when installing multiple control planes.

Le **Guide d'utilisation avancé**, qui s'appuie sur cette base, utilise les mêmes concepts et vous apprend quelques fonctionnalités supplémentaires.

Mais vous devez d'abord lire le **Tutoriel - Guide d'utilisation** (ce que vous êtes en train de lire en ce moment).