<jsp:param name="menuCategoryType" value="user"/>
        <jsp:param name="menuType" value="user"/>
    </jsp:include>
    <div class="content-wrapper">
        <div class="content-header">
            <div class="container-fluid">
                <div class="row mb-2">
                    <div class="col-sm-6">
                        <h1>
                            <la:message key="labels.user_title_details"/>
                        </h1>

static void TF_Reset_Helper(const TF_SessionOptions* opt,
                            const char** containers, int ncontainers,
                            TF_Status* status) {
  std::vector<string> container_names(ncontainers);
  for (int i = 0; i < ncontainers; ++i) {
    container_names[i] = containers[i];
  }

  status->status = Reset(opt->options, container_names);
}

extern "C" {

    development to avoid installing the packages directly on your system (in
    which case remember to change the directory from `/root` to `/tensorflow`
    once you get into the running container so `bazel` can find the `tensorflow`
    workspace).

    you can do this by using the following command. As an example-

    ```bash
    docker run -it --rm -v $PWD:/tmp -w /tmp tensorflow/build:2.15-python3.10

        <jsp:param name="menuType" value="general"/>
    </jsp:include>
    <div class="content-wrapper">
        <div class="content-header">
            <div class="container-fluid">
                <div class="row mb-2">
                    <div class="col-sm-6">
                        <h1>
                            <la:message key="labels.crawler_title_edit"/>
                        </h1>

When a plugin has a complete and valid configuration, the plugin would be looked up in the Plexus container and be ready to perform any operations requested of it.

h3. Execution of the plugin

==============================================================================*/

#include "tensorflow/c/eager/c_api_experimental.h"

#include <cstdint>
#include <memory>
#include <utility>
#include <vector>

#include "absl/container/flat_hash_map.h"
#include "absl/strings/match.h"
#include "absl/time/time.h"
#include "tensorflow/c/c_api.h"
#include "tensorflow/c/eager/c_api_internal.h"
#include "tensorflow/c/eager/tfe_context_internal.h"

另外，请记住，根据您的设置，在某些情况下，您在开始应用程序之前**可能甚至不需要任何先前的步骤**。

 在这种情况下，您就不必担心这些。 🤷

///

### 前面步骤策略的示例

这将在**很大程度上取决于您部署系统的方式**，并且可能与您启动程序、处理重启等的方式有关。

以下是一些可能的想法：

* Kubernetes 中的“Init Container”在应用程序容器之前运行
* 一个 bash 脚本，运行前面的步骤，然后启动您的应用程序
     * 您仍然需要一种方法来启动/重新启动 bash 脚本、检测错误等。

/// tip

我将在以后的章节中为您提供使用容器执行此操作的更具体示例：[容器中的 FastAPI - Docker](docker.md){.internal-link target=_blank}。

///

/// info

🚥 👆 ⚙️ Kubernetes, 👉 🔜 🎲 <a href="https://kubernetes.io/docs/concepts/workloads/pods/init-containers/" class="external-link" target="_blank">🕑 📦</a>.

///

🚥 👆 ⚙️ 💼 📤 🙅‍♂ ⚠ 🏃‍♂ 👈 ⏮️ 📶 **💗 🕰 🔗** (🖼 🚥 👆 🚫 🏃 💽 🛠️, ✋️ ✅ 🚥 💽 🔜), ⤴️ 👆 💪 🚮 👫 🔠 📦 ▶️️ ⏭ ▶️ 👑 🛠️.

### 👁 📦

		c.Fatal("AddUser() for root credential must fail via root STS creds")
	}
}

// SetUpLDAP - expects to setup an LDAP test server using the test LDAP
// container and canned data from https://github.com/minio/minio-ldap-testing
func (s *TestSuiteIAM) SetUpLDAP(c *check, serverAddr string) {
	ctx, cancel := context.WithTimeout(context.Background(), testDefaultTimeout)
	defer cancel()

  }

  FunctionDef fdef() {
    tensorflow::FunctionDef fdef;
    EXPECT_TRUE(GetFunctionDef(func_, &fdef));
    return fdef;
  }

  // logging utility
  template <class Container>
  string ToString(const Container& v) {
    std::stringstream ss;
    ss << "{";
    size_t i = 0;
    for (const auto& e : v) {
      if (i != 0) {
        ss << ", ";
      }
      ss << e.ToString();
      ++i;