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] *}

            descriptor.language === Language.SWIFT -> {
                "* An installed Swift compiler. See which link:{userManualPath}/building_swift_projects.html#sec:swift_supported_tool_chain[Swift tool chains] are supported by Gradle."
            }
            descriptor.language === Language.CPP -> {

     * 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

     * 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

      * Try Google Colab to use TensorFlow.
         * The easiest way to use TF will be to switch to [google colab](https://colab.sandbox.google.com/notebooks/welcome.ipynb#recent=true). You get pre-installed latest stable TF version. Also you can use ```pip install```  to install any other preferred TF version.
         * It has an added advantage since you can you easily switch to different hardware accelerators (cpu, gpu, tpu) as per the task.

-   type: dropdown id: source attributes: label: Source description: Tensorflow
    installed from options: - source - binary validations: required: true

-   type: input id: tfversion attributes: label: Tensorflow Version description:
    placeholder: ex,. tf 2.8 validations: required: true

export MINIO_CI_CD=1
export MINIO_ROOT_USER=minio
export MINIO_ROOT_PASSWORD=minio123
minio server /mnt/data
```

### 5. Test with a regular IAM user

Ensure that `mc` is installed and the configured with the above server with the alias `myminio`.

```sh
# 1. Create a bucket and a user, and upload a file. These operations will succeed.
mc mb myminio/test
mc admin user add myminio foo foobar123

   * 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.X509TrustManager
import okhttp3.Protocol
import org.conscrypt.Conscrypt
import org.conscrypt.ConscryptHostnameVerifier

/**
 * Platform using Conscrypt (conscrypt.org) if installed as the first Security Provider.
 *
 * Requires org.conscrypt:conscrypt-openjdk-uber >= 2.1.0 on the classpath.
 */
class ConscryptPlatform private constructor() : Platform() {

## 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