@rem Set local scope for the variables with windows NT shell
if "%OS%"=="Windows_NT" setlocal

set DIRNAME=%~dp0
if "%DIRNAME%"=="" set DIRNAME=.
@rem This is normally unused
set APP_BASE_NAME=%~n0
set APP_HOME=%DIRNAME%

@rem Resolve any "." and ".." in APP_HOME to make it shorter.
for %%i in ("%APP_HOME%") do set APP_HOME=%%~fi

      throw new AssertionError("Uncaught throwable in " + getName(), uncaughtThrowable);
    }
  }

  /**
   * Causes this thread to call the named void method, and asserts that the call returns normally.
   */
  public void callAndAssertReturns(String methodName, Object... arguments) throws Exception {
    checkNotNull(methodName);
    checkNotNull(arguments);
    sendRequest(methodName, arguments);

When referring to the remote machine, it's common to call it **server**, but also **machine**, **VM** (virtual machine), **node**. Those all refer to some type of remote machine, normally running Linux, where you run programs.

## Install the Server Program { #install-the-server-program }

When you install FastAPI, it comes with a production server, Uvicorn, and you can start it with the `fastapi run` command.

# Return a Response Directly { #return-a-response-directly }

When you create a **FastAPI** *path operation* you can normally return any data from it: a `dict`, a `list`, a Pydantic model, a database model, etc.

By default, **FastAPI** would automatically convert that return value to JSON using the `jsonable_encoder` explained in [JSON Compatible Encoder](../tutorial/encoder.md){.internal-link target=_blank}.

### Normal **FastAPI** { #normal-fastapi }

First, write all your **FastAPI** application as normally:

{* ../../docs_src/extending_openapi/tutorial001_py39.py hl[1,4,7:9] *}

### Generate the OpenAPI schema { #generate-the-openapi-schema }

For example:

* **Audio** or **image processing**.
* **Computer vision**: an image is composed of millions of pixels, each pixel has 3 values / colors, processing that normally requires computing something on those pixels, all at the same time.
* **Machine Learning**: it normally requires lots of "matrix" and "vector" multiplications. Think of a huge spreadsheet with numbers and multiplying all of them together at the same time.

		Secure: stsEndpointURL.Scheme == "https",
	}
	minioClient, err := minio.New(stsEndpointURL.Host, copts)
	if err != nil {
		log.Fatalf("Error initializing client: ", err)
	}

	// Use minIO Client object normally like the regular client.
	fmt.Printf("Calling list objects on bucket named `%s` with temp creds:\n===\n", bucketToList)
	objCh := minioClient.ListObjects(context.Background(), bucketToList, minio.ListObjectsOptions{})

     * </p>
     *
     * @param thumbnailId the identifier for the thumbnail being generated
     * @param cmdList the list of command arguments to execute
     * @return the exit code of the process if it finishes normally; -1 if the process fails or is terminated
     */
    protected int executeCommand(final String thumbnailId, final List<String> cmdList) {
        ProcessDestroyer task = null;
        Process p = null;

And then you can set cookies in that *temporal* response object.

{* ../../docs_src/response_cookies/tutorial002_py39.py hl[1, 8:9] *}

And then you can return any object you need, as you normally would (a `dict`, a database model, etc).

And if you declared a `response_model`, it will still be used to filter and convert the object you returned.

	minioClient, err := minio.New(stsEndpointURL.Host, opts)
	if err != nil {
		log.Fatalf("Error initializing client: %v", err)
	}

	// Use minIO Client object normally like the regular client.
	if bucketToList == "" {
		bucketToList = minioUsername
	}
	fmt.Printf("Calling list objects on bucket named `%s` with temp creds:\n===\n", bucketToList)