* <p>
 * The mojo can be annotated with {@code org.apache.maven.api.di.*} annotations to
 * control the lifecycle of the mojo itself, and to inject other beans.
 * </p>
 * <p>
 * The mojo class can also be injected with an {@link Execute} annotation to specify a
 * forked lifecycle.
 * </p>
 * <p>
 * The {@link Parameter} annotation can be added on fields to inject data

  /**
   * The current state of the service. This should be written with the lock held but can be read
   * without it because it is an immutable object in a volatile field. This is desirable so that
   * methods like {@link #state}, {@link #failureCause} and notably {@link #toString} can be run
   * without grabbing the lock.
   *

       the task as up-to-date, because these two files, which are based on
       the generated index.xml, are outputs, not inputs. We can be sure of
       this because they are deleted in the @BeforeEach method of the
       OsgiTest test class.

       - To enable the benefit of incremental builds, we can ask Gradle
       to ignore these two files when considering whether the classpath

	case windows.DRIVE_FIXED, windows.DRIVE_REMOVABLE, windows.DRIVE_REMOTE, windows.DRIVE_RAMDISK:
		// Recognize "fixed", "removable", "remote" and "ramdisk" drives as proper drives
		// which can be treated as an actual mount-point, rest can be ignored.
		// https://docs.microsoft.com/en-us/windows/desktop/api/fileapi/nf-fileapi-getdrivetypew
		mountPointCache.Store(path, true)
		return true
	}
	mountPointCache.Store(path, false)

# Using MinIO with Veeam

When using Veeam Backup and Replication, you can use S3 compatible object storage such as MinIO as a capacity tier for backups.  This disaggregates storage for the Veeam infrastructure and allows you to retain control of your data. With the ease of use of setup and administration of MinIO, it allows a Veeam backup admin to easily deploy their own object store for capacity tiering.

## Prerequisites

* Any program, any code, **can only do things** when it is being **executed**. So, when there's a **process running**.
* The process can be **terminated** (or "killed") by you, or by the operating system. At that point, it stops running/being executed, and it can **no longer do things**.

   
   Apparently, your CPU model does not support AVX instruction sets. You can still use TensorFlow with the alternatives given below:
   
      * 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.

# ==============================================================================
cat <<EOF
IMPORTANT: These tests ran under docker. This script does not clean up the
container for you! You can delete the container with:

$ docker rm -f tf

You can also execute more commands within the container with e.g.:

$ docker exec tf bazel clean
$ docker exec -it tf bash
EOF

- Once object lock configuration is set to a bucket
  - New objects inherit the retention settings of the bucket object lock configuration automatically
  - Retention headers can be optionally set while uploading objects
  - Once objects are uploaded PutObjectRetention API can be called to change retention settings

# Templating - `Jinja2Templates`

You can use the `Jinja2Templates` class to render Jinja templates.

Read more about it in the [FastAPI docs for Templates](https://fastapi.tiangolo.com/advanced/templates/).

You can import it directly from `fastapi.templating`:

```python
from fastapi.templating import Jinja2Templates
```