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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

	XVMOVQ		X0, X31.B32     // 1f000777
	XVMOVQ		X1, X30.H16     // 3e800777
	XVMOVQ		X2, X29.W8      // 5dc00777
	XVMOVQ		X3, X28.V4      // 7ce00777
	XVMOVQ		X3, X27.Q2      // 7bf00777

	// Move vector element to scalar.
	XVMOVQ		X0, X31.W[7]    // 1fdcff76
	XVMOVQ		X1, X29.W[0]    // 3dc0ff76
	XVMOVQ		X3, X28.V[3]    // 7cecff76
	XVMOVQ		X4, X27.V[0]    // 9be0ff76
	XVMOVQ		X31.W[7], X0    // e0df0377

* 📝 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).