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
```

  ) {
  }

  /**
   * Invoked just prior to initiating a socket connection.
   *
   * This method will be invoked if no existing connection in the [ConnectionPool] can be reused.
   *
   * This can be invoked more than 1 time for a single [Call]. For example, if the response to the
   * [Call.request] is a redirect to a different address, or a connection is retried.
   */
  open fun connectStart(

# `UploadFile` class

You can define *path operation function* parameters to be of the type `UploadFile` to receive files from the request.

You can import it directly from `fastapi`:

```python
from fastapi import UploadFile
```

::: fastapi.UploadFile
    options:
        members:
            - file
            - filename
            - size
            - headers
            - content_type
            - read

We could do better.

We can declare a `UserBase` model that serves as a base for our other models. And then we can make subclasses of that model that inherit its attributes (type declarations, validation, etc).

All the data conversion, validation, documentation, etc. will still work as normally.

When you need `lifespan` to run in your tests, you can use the `TestClient` with a `with` statement:

{* ../../docs_src/app_testing/tutorial004_py39.py hl[9:15,18,27:28,30:32,41:43] *}


You can read more details about the ["Running lifespan in tests in the official Starlette documentation site."](https://www.starlette.dev/lifespan/#running-lifespan-in-tests)

For the deprecated `startup` and `shutdown` events, you can use the `TestClient` as follows:

import com.google.errorprone.annotations.CanIgnoreReturnValue;
import org.jspecify.annotations.Nullable;

/**
 * A {@link ListenableFuture} whose result can be set by a {@link #set(Object)}, {@link
 * #setException(Throwable)} or {@link #setFuture(ListenableFuture)} call. It can also, like any
 * other {@code Future}, be {@linkplain #cancel cancelled}.
 *

 *
 * <p>please use {@link ClassSanityTester#forAllPublicStaticMethods}.
 *
 * <p>If not all classes on the classpath should be covered, {@link #ignoreClasses} can be used to
 * exclude certain classes. As a special case, classes with an underscore in the name (like {@code
 * AutoValue_Foo}) can be excluded using <code>ignoreClasses({@link #UNDERSCORE_IN_NAME})</code>.
 *
 * <p>{@link #setDefault} allows subclasses to specify default values for types.
 *