* **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.

But it's still part of the same **FastAPI** application/web API (it's part of the same "Python Package").

You can create the *path operations* for that module using `APIRouter`.

### Import `APIRouter`

You import it and create an "instance" the same way you would with the class `FastAPI`:

```Python hl_lines="1  3" title="app/routers/users.py"

And of course, it supports the same:

* data validation
* data serialization
* data documentation, etc.

This works the same way as with Pydantic models. And it is actually achieved in the same way underneath, using Pydantic.

/// info

Keep in mind that dataclasses can't do everything Pydantic models can do.

public abstract class DiscreteDomain<C extends Comparable> {

  /**
   * Returns the discrete domain for values of type {@code Integer}.
   *
   * <p>This method always returns the same object. That object is serializable; deserializing it
   * results in the same object too.
   *
   * @since 14.0 (since 10.0 as {@code DiscreteDomains.integers()})
   */
  public static DiscreteDomain<Integer> integers() {
    return IntegerDomain.INSTANCE;

# Cookie Parameters

You can define Cookie parameters the same way you define `Query` and `Path` parameters.

## Import `Cookie`

First import `Cookie`:

//// tab | Python 3.10+

```Python hl_lines="3"
{!> ../../docs_src/cookie_params/tutorial001_an_py310.py!}
```

////

//// tab | Python 3.9+

```Python hl_lines="3"
{!> ../../docs_src/cookie_params/tutorial001_an_py39.py!}
```

////

                    }
                }
            }
        }
    }
}
```

## Additional media types for the main response

You can use this same `responses` parameter to add different media types for the same main response.

For example, you can add an additional media type of `image/png`, declaring that your *path operation* can return a JSON object (with media type `application/json`) or a PNG image:

   *       the same.
   *   <li>The unreserved characters ".", "-", "~", and "_" remain the same.
   *   <li>The general delimiters "@" and ":" remain the same.
   *   <li>The subdelimiters "!", "$", "&amp;", "'", "(", ")", "*", "+", ",", ";", and "=" remain
   *       the same.
   *   <li>The space character " " is converted into %20.

Linux containers run using the same Linux kernel of the host (machine, virtual machine, cloud server, etc). This just means that they are very lightweight (compared to full virtual machines emulating an entire operating system).

This will be especially useful when you use it in a **large code base** where you use **the same dependencies** over and over again in **many *path operations***.

## To `async` or not to `async`

As dependencies will also be called by **FastAPI** (the same as your *path operation functions*), the same rules apply while defining your functions.

    assertThat(network).isNotEqualTo(g2);
  }

  // Node sets are the same, but edge sets differ.
  @Test
  public void equivalent_edgeSetsDiffer() {
    network.addEdge(N1, N2, E12);

    MutableNetwork<Integer, String> g2 = createNetwork(edgeType);
    g2.addEdge(N1, N2, E13);

    assertThat(network).isNotEqualTo(g2);
  }

  // Node/edge sets are the same, but node/edge connections differ due to edge type.
  @Test