* Nếu bạn biết kiểu dữ liệu Python, bạn biết cách sử dụng Pydantic.
* Sử dụng tốt với **<abbr title="Môi trường phát triển tích hợp, tương tự như một trình soạn thảo code">IDE</abbr>/<abbr title="Một chương trình kiểm tra code lỗi">linter</abbr>/não của bạn**:

    * Bởi vì các cấu trúc dữ liệu của Pydantic chỉ là các instances của class bạn định nghĩa; auto-completion, linting, mypy và trực giác của bạn nên làm việc riêng biệt với những dữ liệu mà bạn đã validate.

* **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。

#include "tensorflow/core/lib/llvm_rtti/llvm_rtti.h"
#include "tensorflow/core/platform/errors.h"

namespace tensorflow {
namespace gradients {
namespace {

// TODO(b/172558015): Using the pointer address as the identifier for the tensor
// may lead to collisions. Introduce another way to get a unique id for this
// tensor.
int64_t ToId(const AbstractTensorHandle* t) {

    checkNotNull(file);
    checkNotNull(charset);
    return new BufferedReader(new InputStreamReader(new FileInputStream(file), charset));
  }

  /**
   * Returns a buffered writer that writes to a file using the given character set.
   *
   * <p><b>{@link java.nio.file.Path} equivalent:</b> {@link
   * java.nio.file.Files#newBufferedWriter(java.nio.file.Path, Charset,
   * java.nio.file.OpenOption...)}.

    checkNotNull(file);
    checkNotNull(charset);
    return new BufferedReader(new InputStreamReader(new FileInputStream(file), charset));
  }

  /**
   * Returns a buffered writer that writes to a file using the given character set.
   *
   * <p><b>{@link java.nio.file.Path} equivalent:</b> {@link
   * java.nio.file.Files#newBufferedWriter(java.nio.file.Path, Charset,
   * java.nio.file.OpenOption...)}.

		return errDiskNotFound
	}
	return err
}

// Abstracts a remote disk.
type storageRESTClient struct {
	endpoint   Endpoint
	restClient *rest.Client
	gridConn   *grid.Subroute
	diskID     atomic.Pointer[string]

	diskInfoCache *cachevalue.Cache[DiskInfo]
}

// Retrieve location indexes.
func (client *storageRESTClient) GetDiskLoc() (poolIdx, setIdx, diskIdx int) {

	MaxErrResponseSize int64

	// Avoid metrics update if set to true
	NoMetrics bool

	// TraceOutput will print debug information on non-200 calls if set.
	TraceOutput io.Writer // Debug trace output

	httpClient *http.Client
	url        *url.URL
	auth       func() string

	sync.RWMutex // mutex for lastErr
	lastErr      error
	lastErrTime  time.Time
}

	NEG	X5					// b3025040
	NEG	X5, X6					// 33035040
	NEGW	X5					// bb025040
	NEGW	X5, X6					// 3b035040

	// This jumps to the second instruction in the function (the
	// first instruction is an invisible stack pointer adjustment).
	JMP	start					// JMP	2

	JMP	2(PC)					// 6f008000
	JMP	(X5)					// 67800200
	JMP	4(X5)					// 67804200

	// CALL and JMP to symbol are encoded as JAL (using LR or ZERO

   * double the index of the entry in entrySet.asList.)
   *
   * The basic data structure is described in https://en.wikipedia.org/wiki/Open_addressing.
   * The pointer to a key is stored in hashTable[Hashing.smear(key.hashCode()) % table.length],
   * save that if that location is already full, we try the next index, and the next, until we

- Added new option to the `InterPodAffinity` scheduler plugin to ignore existing
  pods` preferred inter-pod affinities if the incoming pod has no preferred inter-pod
  affinities. This option can be used as an optimization for higher scheduling throughput
  (at the cost of an occasional pod being scheduled non-optimally/violating existing
  pods preferred inter-pod affinities). To enable this scheduler option, set the