This is normally called a **webhook**.

## Webhooks steps { #webhooks-steps }

The process normally is that **you define** in your code what is the message that you will send, the **body of the request**.

You also define in some way at which **moments** your app will send those requests or events.

And **your users** define in some way (for example in a web dashboard somewhere) the **URL** where your app should send those requests.

common:mkl --define=build_with_mkl=true --define=enable_mkl=true
common:mkl --define=tensorflow_mkldnn_contraction_kernel=0
common:mkl --define=build_with_openmp=true
common:mkl -c opt

# config to build OneDNN backend with a user specified threadpool.
common:mkl_threadpool --define=build_with_mkl=true --define=enable_mkl=true
common:mkl_threadpool --define=tensorflow_mkldnn_contraction_kernel=0

};

TEST_F(CApiFunctionTest, OneOp_ZeroInputs_OneOutput) {
  /*
   *                constant
   *                   |
   *                   v
   */
  // Define
  TF_Operation* c = ScalarConst(10, func_graph_, s_, "scalar10");
  Define(-1, {}, {}, {c}, {});

  // Use, run, and verify
  TF_Operation* func_op = Use({});
  Run({}, func_op, 10);
  VerifyFDef({"scalar10_0"}, {}, {{"scalar10", DT_INT32}},

## Import `File` and `Form` { #import-file-and-form }

{* ../../docs_src/request_forms_and_files/tutorial001_an_py310.py hl[3] *}

## Define `File` and `Form` parameters { #define-file-and-form-parameters }

Create file and form parameters the same way you would for `Body` or `Query`:

{* ../../docs_src/request_forms_and_files/tutorial001_an_py310.py hl[10:12] *}

# Lifespan Events { #lifespan-events }

You can define logic (code) that should be executed before the application **starts up**. This means that this code will be executed **once**, **before** the application **starts receiving requests**.

The same way, you can define logic (code) that should be executed when the application is **shutting down**. In this case, this code will be executed **once**, **after** having handled possibly **many requests**.

You can import it directly from `fastapi`:

```python
from fastapi import Request
```

/// tip

When you want to define dependencies that should be compatible with both HTTP and WebSockets, you can define a parameter that takes an `HTTPConnection` instead of a `Request` or a `WebSocket`.

///

So, you can declare deeply nested JSON "objects" with specific attribute names, types and validations.

All that, arbitrarily nested.

### Define a submodel { #define-a-submodel }

For example, we can define an `Image` model:

{* ../../docs_src/body_nested_models/tutorial004_py310.py hl[7:9] *}

### Use the submodel as a type { #use-the-submodel-as-a-type }

		switch in.Stack.Text() {
		case "else", "endif", "ifdef", "ifndef", "line":
			// Press on.
		default:
			return false
		}
	}
	switch in.Stack.Text() {
	case "define":
		in.define()
	case "else":
		in.else_()
	case "endif":
		in.endif()
	case "ifdef":
		in.ifdef(true)
	case "ifndef":
		in.ifdef(false)
	case "include":
		in.include()
	case "line":

///

## Import `File` { #import-file }

Import `File` and `UploadFile` from `fastapi`:

{* ../../docs_src/request_files/tutorial001_an_py310.py hl[3] *}

## Define `File` Parameters { #define-file-parameters }

Create file parameters the same way you would for `Body` or `Form`:

{* ../../docs_src/request_files/tutorial001_an_py310.py hl[9] *}

/// info

`File` is a class that inherits directly from `Form`.

It is provided directly by Starlette, but you can import it from `fastapi`:

```python
from fastapi import WebSocket
```

/// tip

When you want to define dependencies that should be compatible with both HTTP and WebSockets, you can define a parameter that takes an `HTTPConnection` instead of a `Request` or a `WebSocket`.

///

::: fastapi.WebSocket
    options:
        members:
            - scope