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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.

{* ../../docs_src/response_model/tutorial001_01_py310.py hl[16,21] *}

FastAPI will use this return type to:

* **Validate** the returned data.

        Keras training loops like `fit`/`evaluate`, the unreduced vector loss is
        passed to the optimizer but the reported loss will be a scalar value.
    *   `SUM`: Scalar sum of weighted losses. 4. `SUM_OVER_BATCH_SIZE`: Scalar
        `SUM` divided by number of elements in losses. This reduction type is
        not supported when used with `tf.distribute.Strategy` outside of

	VIOTAM		V2, V3				// d7212852
	VIOTAM		V2, V0, V3			// d7212850
	VIDV		V3				// d7a10852
	VIDV		V0, V3				// d7a10850

	// 31.16.1: Integer Scalar Move Instructions
	VMVXS		V2, X10				// 57252042
	VMVSX		X10, V2				// 57610542

	// 31.16.2: Floating-Point Scalar Move Instructions
	VFMVFS		V2, F10				// 57152042
	VFMVSF		F10, V2				// 57510542

	// 31.16.3: Vector Slide Instructions

* 📝 Tweak default suggested configs for generating clients. PR [#10736](https://github.com/tiangolo/fastapi/pull/10736) by [@tiangolo](https://github.com/tiangolo).

### Internal

* 🔧 Update sponsors, add Scalar. PR [#10728](https://github.com/tiangolo/fastapi/pull/10728) by [@tiangolo](https://github.com/tiangolo).