Let's create a dependency `get_current_user`.

Remember that dependencies can have sub-dependencies?

`get_current_user` will have a dependency with the same `oauth2_scheme` we created before.

The same as we were doing before in the *path operation* directly, our new dependency `get_current_user` will receive a `token` as a `str` from the sub-dependency `oauth2_scheme`:

{* ../../docs_src/security/tutorial002_an_py310.py hl[25] *}

# Set to false to disable. If disabled, issues still need to be closed manually, but will remain marked as stale.
daysUntilClose: 15

# Only issues or pull requests with all of these labels are check if stale. Defaults to `[]` (disabled)
onlyLabels: []

# Issues or Pull Requests with these labels will never be considered stale. Set to `[]` to disable
exemptLabels:
  - "security"

When a **container** is started, it will run that command/program (although you can override it and make it run a different command/program).

A container is running as long as the **main process** (command or program) is running.

A container normally has a **single process**, but it's also possible to start subprocesses from the main process, and that way you will have **multiple processes** in the same container.

ret=$?
if [ $ret -eq 0 ]; then
	echo "BUG: PutObject to bucket: multi-key-poc with invalid sse-kms should fail. Succeeded"
	exit 1
fi

kill $pid

  - type: textarea
    id: current-behavior
    attributes:
      label: Current Behavior
      description: Tell us what happens
    validations:
      required: true
  - type: textarea
    id: expected-behavior
    attributes:
      label: Expected Behavior
      description: Tell us what should happen
    validations:
      required: true
  - type: textarea
    id: context
    attributes:

   
   
   Therefore on any CPU that does not have these instruction sets, either CPU or GPU version of TF will fail to load.
   
   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.

            // Test with longer file ID (should copy only first 16 bytes)
            byte[] longFileId = new byte[32];
            Arrays.fill(longFileId, (byte) 0xCD);
            request.setFileId(longFileId);

            Arrays.fill(buffer, (byte) 0);
            bytesWritten = request.writeBytesWireFormat(buffer, 0);

            assertEquals(49, bytesWritten);

It had the best performance benchmarks at the time (only surpassed by Starlette).

At first, it didn't have an automatic API documentation web UI, but I knew I could add Swagger UI to it.

It had a dependency injection system. It required pre-registration of components, as other tools discussed above. But still, it was a great feature.

### Create a custom `GzipRoute` class { #create-a-custom-gziproute-class }

Next, we create a custom subclass of `fastapi.routing.APIRoute` that will make use of the `GzipRequest`.

This time, it will overwrite the method `APIRoute.get_route_handler()`.

This method returns a function. And that function is what will receive a request and return a response.

Here we use it to create a `GzipRequest` from the original request.

Just functions that look the same as the *path operation functions*.

But still, it is very powerful, and allows you to declare arbitrarily deeply nested dependency "graphs" (trees).

/// tip

All this might not seem as useful with these simple examples.

But you will see how useful it is in the chapters about **security**.

And you will also see the amounts of code it will save you.