For that, you can use the `WSGIMiddleware` and use it to wrap your WSGI application, for example, Flask, Django, etc.

## Using `WSGIMiddleware` { #using-wsgimiddleware }

/// info

This requires installing `a2wsgi` for example with `pip install a2wsgi`.

///

You need to import `WSGIMiddleware` from `a2wsgi`.

Then wrap the WSGI (e.g. Flask) app with the middleware.

* **`200 - 299`** are for "Successful" responses. These are the ones you would use the most.
    * `200` is the default status code, which means everything was "OK".
    * Another example would be `201`, "Created". It is commonly used after creating a new record in the database.

   * java.io.Serializable} instance, and is thus declared an {@code Object}. For example, it might
   * be declared as a {@code List}.
   *
   * <p>Note also that serialization is not in general required to return an object that is
   * {@linkplain Object#equals equal} to the original, nor is it required to return even an object
   * of the same class. For example, if sublists of {@code MyList} instances were serializable,

#
map:{
    # example for implicit classification
    #; Flg = list:{
    #    ; map:{topComment=general boolean classification for every flg-column; codeType=Number}
    #    ; map:{code=1; name=True ; alias=Checked  ; comment=means yes; sisterCode=true}
    #    ; map:{code=0; name=False; alias=Unchecked; comment=means no ; sisterCode=false}
    #}
    # example for table classification
    #; MemberStatus = list:{

# Query Parameters and String Validations { #query-parameters-and-string-validations }

**FastAPI** allows you to declare additional information and validation for your parameters.

Let's take this application as example:

{* ../../docs_src/query_params_str_validations/tutorial001_py310.py hl[7] *}

that can send requests to a Gradle daemon.
Each daemon runs one request at a time. Generally speaking, the daemons only act in response to a request from a client and do not take any action on their own.
There are some background actions that the daemon takes, for example monitoring system memory, watching for file changes or cleaning up caches.

### **4.1 Spark Pi**

Test the Spark installation by running the following compute intensive example, which calculates pi by “throwing darts” at a circle. The program generates points in the unit square ((0,0) to (1,1)) and counts how many points fall within the unit circle within the square. The result approximates pi.

Follow these steps to run the Spark Pi example:

- Login as user **‘spark’**.

 * @author Ben Yu
 * @since 1.0
 */
@GwtCompatible
public final class Throwables {
  private Throwables() {}

  /**
   * Throws {@code throwable} if it is an instance of {@code declaredType}. Example usage:
   *
   * <pre>
   * for (Foo foo : foos) {
   *   try {
   *     foo.bar();
   *   } catch (BarException | RuntimeException | Error t) {
   *     failure = t;
   *   }
   * }

The `TestClient` is based on [HTTPX](https://www.python-httpx.org), and luckily, we can use it directly to test the API.

## Example { #example }

For a simple example, let's consider a file structure similar to the one described in [Bigger Applications](../tutorial/bigger-applications.md) and [Testing](../tutorial/testing.md):

```
.
├── app
│   ├── __init__.py

For example, you could decide to read and validate the request with your own code, without using the automatic features of FastAPI with Pydantic, but you could still want to define the request in the OpenAPI schema.

You could do that with `openapi_extra`:

{* ../../docs_src/path_operation_advanced_configuration/tutorial006_py310.py hl[19:36, 39:40] *}