!!! tip
    You'll see what other "things", apart from functions, can be used as dependencies in the next chapter.

Whenever a new request arrives, **FastAPI** will take care of:

* Calling your dependency ("dependable") function with the correct parameters.
* Get the result from your function.
* Assign that result to the parameter in your *path operation function*.

```mermaid
graph TB

    rect rgba(0, 255, 0, .1)
        code ->> function: say_hi(name="Camila")
        function ->> execute: führe Code der Funktion aus
        execute ->> code: gib das Resultat zurück
    end

    rect rgba(0, 255, 255, .1)
        code ->> function: say_hi(name="Camila")
        function ->> code: gib das gespeicherte Resultat zurück
    end

    rect rgba(0, 255, 0, .1)

{!../../../docs_src/custom_response/tutorial008.py!}
```

1. This is the generator function. It's a "generator function" because it contains `yield` statements inside.
2. By using a `with` block, we make sure that the file-like object is closed after the generator function is done. So, after it finishes sending the response.

### Other utility functions

Any other utility function that you call directly can be created with normal `def` or `async def` and FastAPI won't affect the way you call it.

This is in contrast to the functions that FastAPI calls for you: *path operation functions* and dependencies.

participant code as Code
participant function as say_hi()
participant execute as Execute function

    rect rgba(0, 255, 0, .1)
        code ->> function: say_hi(name="Camila")
        function ->> execute: execute function code
        execute ->> code: return the result
    end

    rect rgba(0, 255, 255, .1)
        code ->> function: say_hi(name="Camila")
        function ->> code: return stored result
    end

# Response Model - Return Type

You can declare the type used for the response by annotating the *path operation function* **return type**.

You can use **type annotations** the same way you would for input data in function **parameters**, you can use Pydantic models, lists, dictionaries, scalar values like integers, booleans, etc.

=== "Python 3.10+"

    ```Python hl_lines="16  21"
    {!> ../../../docs_src/response_model/tutorial001_01_py310.py!}
    ```

        "@com_google_absl//absl/strings",
    ],
)

cc_library(
    name = "graph_function",
    srcs = ["graph_function.cc"],
    hdrs = [
        "graph_function.h",
    ],
    visibility = [
        "//tensorflow:internal",
    ],
    deps = [
        ":abstract_function",
        "//tensorflow/core:framework",
        "//tensorflow/core/platform:refcount",

        execute ->> code: return the result
    end

    rect rgba(0, 255, 255, .1)
        code ->> function: say_hi(name="Camila")
        function ->> code: return stored result
    end

    rect rgba(0, 255, 0, .1)
        code ->> function: say_hi(name="Rick")
        function ->> execute: execute function code
        execute ->> code: return the result
    end

### Step 4: define the **path operation function**

This is our "**path operation function**":

* **path**: is `/`.
* **operation**: is `get`.
* **function**: is the function below the "decorator" (below `@app.get("/")`).

```Python hl_lines="7"
{!../../../docs_src/first_steps/tutorial001.py!}
```

This is a Python function.

        functionCall: FirFunctionCall,
        mode: KtConstantEvaluationMode,
    ): FirLiteralExpression<*>? {
        val function = functionCall.getOriginalFunction() as? FirSimpleFunction ?: return null

        val opr1 = evaluate(functionCall.explicitReceiver, mode) ?: return null
        opr1.evaluate(function)?.let {
            return it.adjustType(functionCall.resolvedType)
        }