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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

    if (dst->dims() != 0) {
      return InvalidArgument(
          "Malformed TF_RESOURCE tensor: expected a scalar, got a tensor with "
          "shape ",
          dst->shape().DebugString());
    }
    *dst = tensorflow::Tensor(tensorflow::DT_RESOURCE, dst->shape());
    if (!dst->scalar<tensorflow::ResourceHandle>()().ParseFromString(
            string(static_cast<const char*>(tensor_interface->Data()),

	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