///

The `status_code` parameter receives a number with the HTTP status code.

/// info

`status_code` can alternatively also receive an `IntEnum`, such as Python's <a href="https://docs.python.org/3/library/http.html#http.HTTPStatus" class="external-link" target="_blank">`http.HTTPStatus`</a>.

///

It will:

                + "      mvn install:install-file -DgroupId=aGroupId -DartifactId=anArtifactId -Dversion=aVersion "
                + "-Dclassifier=aClassifier -Dpackaging=jar -Dfile=/path/to/file" + LS + LS
                + "  Alternatively, if you host your own repository you can deploy the file there: " + LS
                + "      mvn deploy:deploy-file -DgroupId=aGroupId -DartifactId=anArtifactId"

            sb.append(" -Dfile=/path/to/file");
            sb.append(LS);

            // If people want to deploy it
            sb.append(LS);
            sb.append(indentation);
            sb.append("Alternatively, if you host your own repository you can deploy the file there: ");
            sb.append(LS);
            sb.append(indentation);
            sb.append("    mvn deploy:deploy-file -DgroupId=");

</div>

The response would be something like:

```JSON
{
    "message": "Hello World",
    "root_path": "/api/v1"
}
```

### Setting the `root_path` in the FastAPI app

Alternatively, if you don't have a way to provide a command line option like `--root-path` or equivalent, you can set the `root_path` parameter when creating your FastAPI app:

{* ../../docs_src/behind_a_proxy/tutorial002.py hl[3] *}

    and
    [GPU developer Dockerfile](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/dockerfiles/dockerfiles/devel-gpu.Dockerfile)
    for the required packages. Alternatively, use the said
    [tensorflow/build Docker images](https://hub.docker.com/r/tensorflow/build)
    (`tensorflow/tensorflow:devel` and `tensorflow/tensorflow:devel-gpu` are no

        mean, var = tf.nn.moments(self.kernel, axes=[0, 1, 2], keepdims=True)
        return self.convolution_op(inputs, (self.kernel - mean) / tf.sqrt(var +
        1e-10))` Alternatively, you can override `convolution_op`: `python class
        StandardizedConv2D(tf.keras.Layer): def convolution_op(self, inputs,
        kernel): mean, var = tf.nn.moments(kernel, axes=[0, 1, 2],