lines(
			"#ifdef A",
			"#ifdef B",
			"#define C 1234",
			"#else",
			"#define C 5678",
			"#endif",
			"#endif",
			"C",
		),
		"C.\n",
	},
	{
		"nested #define",
		lines(
			"#define A #define B THIS",
			"A",
			"B",
		),
		"THIS.\n",
	},
	{
		"nested #define with args",
		lines(
			"#define A #define B(x) x",
			"A",
			"B(THIS)",
		),

# Security Tools

When you need to declare dependencies with OAuth2 scopes you use `Security()`.

But you still need to define what is the dependable, the callable that you pass as a parameter to `Depends()` or `Security()`.

There are multiple tools that you can use to create those dependables, and they get integrated into OpenAPI so they are shown in the automatic docs UI, they can be used by automatically generated clients and SDKs, etc.

///

{* ../../docs_src/security/tutorial005_an_py310.py hl[157] *}

## Declare scopes in *path operations* and dependencies { #declare-scopes-in-path-operations-and-dependencies }

Now we declare that the *path operation* for `/users/me/items/` requires the scope `items`.

For this, we import and use `Security` from `fastapi`.

You can declare multiple path parameters and query parameters at the same time, **FastAPI** knows which is which.

And you don't have to declare them in any specific order.

They will be detected by name:

{* ../../docs_src/query_params/tutorial004_py310.py hl[6,8] *}

## Required query parameters { #required-query-parameters }

+# define __attribute_copy__(arg)
+#endif
+
 #ifndef __ASSEMBLER__
 /* GCC understands weak symbols and aliases; use its interface where
    possible, instead of embedded assembly language.  */
@@ -114,7 +119,8 @@
 /* Define ALIASNAME as a strong alias for NAME.  */
 # define strong_alias(name, aliasname) _strong_alias(name, aliasname)
 # define _strong_alias(name, aliasname) \

But if you return a `Response` directly (or any subclass, like `JSONResponse`), the data won't be automatically converted (even if you declare a `response_model`), and the documentation won't be automatically generated (for example, including the specific "media type", in the HTTP header `Content-Type` as part of the generated OpenAPI).

Hug inspired parts of APIStar, and was one of the tools I found most promising, alongside APIStar.

Hug helped inspiring **FastAPI** to use Python type hints to declare parameters, and to generate a schema defining the API automatically.

Hug inspired **FastAPI** to declare a `response` parameter in functions to set headers and cookies.

///

cluster_deadline                (duration)  set the deadline for cluster readiness check (default: '10s')
cors_allow_origin               (csv)       set comma separated list of origins allowed for CORS requests (default: '*')
remote_transport_deadline       (duration)  set the deadline for API requests on remote transports while proxying between federated instances e.g. "2h" (default: '2h')

///

/// tip

If you are wondering, "AlexNet", "ResNet", and "LeNet" are just names of Machine Learning <abbr title="Technically, Deep Learning model architectures">models</abbr>.

///

### Declare a *path parameter* { #declare-a-path-parameter }

Then create a *path parameter* with a type annotation using the enum class you created (`ModelName`):

{* ../../docs_src/path_params/tutorial005.py hl[16] *}

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

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