researchers push the state-of-the-art in ML and developers easily build and
deploy ML-powered applications.

TensorFlow was originally developed by researchers and engineers working within
the Machine Intelligence team at Google Brain to conduct research in machine
learning and neural networks. However, the framework is versatile enough to be
used in other areas as well.

TensorFlow provides stable [Python](https://www.tensorflow.org/api_docs/python)

  - [beta] PodDisruptionBudget has been promoted to beta, can be used to safely drain nodes while respecting application SLO's ([docs](http://kubernetes.io/docs/tasks/administer-cluster/safely-drain-node/)) ([kubernetes/features#85](https://github.com/kubernetes/enhancements/issues/85))
- **UI**

            // in case of mvnsh the context.logger != context.invokerRequest.parserRequest.logger
            List<Logger.Entry> entries =
                    context.invokerRequest.parserRequest().logger().drain();
            printErrors(
                    context,
                    context.invokerRequest
                            .parserRequest()
                            .args()

	d.maxSleep = maxWait
	d.cycle = make(chan struct{})
	return nil
}

const (
	// ILMExpiry - audit trail for ILM expiry
	ILMExpiry = "ilm:expiry"
	// ILMFreeVersionDelete - audit trail for ILM free-version delete
	ILMFreeVersionDelete = "ilm:free-version-delete"
	// ILMTransition - audit trail for ILM transitioning.
	ILMTransition = " ilm:transition"
)

* **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。

    ],
)

tf_cuda_library(
    name = "c_api_test_util",
    testonly = 1,
    srcs = ["c_api_test_util.cc"],
    hdrs = ["c_api_test_util.h"],
    visibility = [
        "//learning/brain:__subpackages__",
        "//tensorflow:__subpackages__",
    ],
    deps = [
        ":c_api",
        ":c_api_experimental",
        ":c_api_internal",
        "//tensorflow/c:c_test_util",

* **更簡單**：
    * 不需要學習新的 micro-language 來定義結構。
    * 如果你知道 Python 型別，你就知道如何使用 Pydantic。
* 和你的 **<abbr title="Integrated Development Environment，和程式碼編輯器類似">IDE</abbr>/<abbr title="一個檢查程式碼錯誤的工具">linter</abbr>/brain** 都能好好配合：
    * 因為 Pydantic 的資料結構其實就是你自己定義的類別實例，所以自動補齊、linting、mypy 以及你的直覺都能很好地在經過驗證的資料上發揮作用。
* 驗證**複雜結構**：
    * 使用 Pydantic 模型時，你可以把資料結構分層設計，並且用 Python 的 `List` 和 `Dict` 等型別來定義。

- Kubectl/drain: add disable-eviction option.Force drain to use delete, even if eviction is supported. This will bypass checking PodDisruptionBudgets, and should be used with caution. ([#85571](https://github.com/kubernetes/kubernetes/pull/85571), [@michaelgugino](https://github.com/michaelgugino)) [SIG CLI]

				quitFlag = true
			}
		}
	}
	close(writer.doneCh)

	recordStagingTicker.Stop()
	keepAliveTicker.Stop()
	if progressTicker != nil {
		progressTicker.Stop()
	}

	// Whatever drain the payloadCh to prevent from memory leaking.
	for len(writer.payloadCh) > 0 {
		payload := <-writer.payloadCh
		bufPool.Put(payload)
	}
}

// Sends a single whole record.

* Make etcd cache size configurable ([#23914](https://github.com/kubernetes/kubernetes/pull/23914), [@jsravn](https://github.com/jsravn))
* Drain pods created from ReplicaSets in 'kubectl drain' ([#23689](https://github.com/kubernetes/kubernetes/pull/23689), [@maclof](https://github.com/maclof))