2. By using a `with` block, we make sure that the file-like object is closed after the generator function is done. So, after it finishes sending the response.
3. This `yield from` tells the function to iterate over that thing named `file_like`. And then, for each part iterated, yield that part as coming from this generator function (`iterfile`).

   with its <<<DefaultPluginManagementInjector>>> implementation
   ({{{./xref/org/apache/maven/model/management/DefaultPluginManagementInjector.html}source}})

   ** <(optional)> lifecycle bindings injection: <<<LifecycleBindingsInjector>>> ({{{./apidocs/org/apache/maven/model/plugin/LifecycleBindingsInjector.html}javadoc}}),
   with its <<<DefaultLifecycleBindingsInjector>>> implementation in maven-core

// This call may not block for execution of ops enqueued concurrently with this
// call.
TF_CAPI_EXPORT extern void TFE_ExecutorWaitForAllPendingNodes(
    TFE_Executor*, TF_Status* status);

// When an error happens, any pending operations are discarded, and newly issued
// ops return an error. This call clears the error state and re-enables
// execution of newly issued ops.
//

    // 3. This connection's server certificate's must cover the new host.
    if (address.hostnameVerifier !== OkHostnameVerifier) return false
    if (!supportsUrl(address.url)) return false

    // 4. Certificate pinning must match the host.
    try {
      address.certificatePinner!!.check(address.url.host, handshake()!!.peerCertificates)
    } catch (_: SSLPeerUnverifiedException) {
      return false
    }

    * 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.
* Validate **các cấu trúc phức tạp**:
    * Sử dụng các models Pydantic phân tầng, `List` và `Dict` của Python `typing`,...

    * 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。
    * 你可以拥有深度**嵌套的 JSON** 对象并对它们进行验证和注释。
* **可扩展**:

                   std::vector<std::string>* control_ret_node_names,
                   bool* control_rets_updated) override {
    // Inject failure to function instantiation if finding a node that contains
    // the given node name (error_node_) and requested device (error_device_).
    for (const auto node : graph->get()->nodes()) {
      if (node->name().find(error_node_) != string::npos &&

    TF_Status status;
    // Let this custom device choose the device to pin this op on if it
    // implements the pinning function.
    if (device_.shall_pin_to_this_device != nullptr) {
      return device_.shall_pin_to_this_device(tensorflow::wrap(op), &status);
    }
    return errors::Unimplemented("No custom device pinning implementation.");
  }

 private:
  TFE_Context* context_;
  TFE_CustomDevice device_;
  void* info_;

	// value of true indicates that the list was truncated. The list can be truncated
	// if the number of multipart uploads exceeds the limit allowed or specified
	// by max uploads.
	IsTruncated bool

	// List of all pending uploads.
	Uploads []MultipartInfo

	// When a prefix is provided in the request, The result contains only keys
	// starting with the specified prefix.
	Prefix string

// containing any run metadata information accumulated so far and clears this
// information.
// If async mode is enabled, this call blocks till all currently pending ops are
// done.
TF_CAPI_EXPORT extern void TFE_ContextExportRunMetadata(TFE_Context* ctx,
                                                        TF_Buffer* buf,