```bash
    python tf_doctest.py --file=<file_path>
    ```

    This will run it using your installed version of TensorFlow. To be sure
    you're running the same code that you're testing:

    *   Use an up to date [tf-nightly](https://pypi.org/project/tf-nightly/)
        `pip install -U tf-nightly`
    *   Rebase your pull request onto a recent pull from

## Prerequisites

Please make sure the following packages are already installed via `dnf` or `apt`: 

- `tuned`
- `curl`

### Install `tuned.conf` performance profile

#### Step 1 - download `tuned.conf` from the referenced link
```
wget https://raw.githubusercontent.com/minio/minio/master/docs/tuning/tuned.conf
```

#### Step 2 - install tuned.conf as supported performance profile on all nodes
```

Previously, it was recommended to use `WSGIMiddleware` from `fastapi.middleware.wsgi`, but it is now deprecated.

It’s advised to use the `a2wsgi` package instead. The usage remains the same.

Just ensure that you have the `a2wsgi` package installed and import `WSGIMiddleware` correctly from `a2wsgi`.

///

## Check it { #check-it }

Now, every request under the path `/v1/` will be handled by the Flask application.

## Prerequisites

- Prometheus and MinIO configured as explained in [document here](https://github.com/minio/minio/blob/master/docs/metrics/prometheus/README.md).
- Grafana installed as explained [here](https://grafana.com/grafana/download).

## MinIO Grafana Dashboard

See https://www.elastic.co/community/security for more information.

Please fill in the following details to help us reproduce the bug:
-->

**Elasticsearch version** (`bin/elasticsearch --version`):

**Plugins installed**: []

**JVM version** (`java -version`):

**OS version** (`uname -a` if on a Unix-like system):

**Description of the problem including expected versus actual behavior**:

**Steps to reproduce**:

about: Use this template for reporting Lite ops you are using or missing
labels: 'comp:lite'

---

**System information**
- OS Platform and Distribution (e.g., Linux Ubuntu 16.04):
- TensorFlow installed from (source or binary):
- TensorFlow version (or github SHA if from source):


**Provide the text output from tflite_convert**

```
# Copy and paste here
```

**Standalone code to reproduce the issue** 

   * MyTag myTag = original.tag(MyTag.class);
   * if (myTag != null) {
   *   copy.tag(MyTag.class, () -> myTag);
   * }
   * ```
   *
   * If any event listeners were installed on this call with [addEventListener], they will not be
   * installed on this copy.
   */
  public override fun clone(): Call

  fun interface Factory {
    fun newCall(request: Request): Call
  }

import javax.net.ssl.SSLSocketFactory
import javax.net.ssl.TrustManagerFactory
import javax.net.ssl.X509TrustManager
import okhttp3.Protocol

/**
 * Platform using OpenJSSE (https://github.com/openjsse/openjsse) if installed as the first
 * Security Provider.
 *
 * Requires org.openjsse:openjsse >= 1.1.0 on the classpath.
 */
class OpenJSSEPlatform private constructor() : Platform() {
  private val provider: Provider =

Pydantic v2 includes everything from Pydantic v1 as a submodule `pydantic.v1`. But this is no longer supported in versions above Python 3.13.

This means that you can install the latest version of Pydantic v2 and import and use the old Pydantic v1 components from this submodule, as if you had the old Pydantic v1 installed.

{* ../../docs_src/pydantic_v1_in_v2/tutorial001_an_py310.py hl[1,4] *}

     * correctly installed
     *
     * <p>{@link #transitioned}: is set by {@link #transitionService} to indicate that some
     * transition has been performed.
     *
     * <p>Together, they allow us to enforce that all services have their listeners installed prior
     * to any service performing a transition, then we can fail in the ServiceManager constructor