*                   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}},
             {{"scalar10_0:output:0", "scalar10"}}, {});
}

TEST_F(CApiFunctionTest, OneOp_OneInput_OneOutput) {
  /*

  TF_Operation* scalar3 = TF_GraphOperationByName(graph, "imported3/scalar");
  TF_Operation* feed3 = TF_GraphOperationByName(graph, "imported3/feed");
  TF_Operation* neg3 = TF_GraphOperationByName(graph, "imported3/neg");
  ASSERT_TRUE(scalar3 != nullptr);
  ASSERT_TRUE(feed3 != nullptr);
  ASSERT_TRUE(neg3 != nullptr);

  // Check that newly-imported scalar and feed have control deps (neg3 will

  TFE_OpSetAttrType(fill_op, "T", TF_FLOAT);
  TFE_OpSetAttrType(fill_op, "Tshape", TF_INT32);

  float five = 5.0;
  TFE_TensorHandle* scalar = TestScalarTensorHandle(tfe_context_, five);
  TF_Tensor* scalarTensor = TFE_TensorHandleResolve(scalar, status_);
  CHECK_EQ(TF_OK, TF_GetCode(status_)) << TF_Message(status_);
  CheckOutputShapes(fill_op,

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&y.s) return s } // Subtract sets s = x - y mod l, and returns s. func (s *Scalar) Subtract(x, y *Scalar) *Scalar { // s = -1 * y + x mod l fiatScalarSub(&s.s, &x.s, &y.s) return s } // Negate sets s = -x mod l, and returns s. func (s *Scalar) Negate(x *Scalar) *Scalar { // s = -1 * x + 0 mod l fiatScalarOpp(&s.s, &x.s) return s } // Multiply sets s = x * y mod l, and returns s. func (s *Scalar) Multiply(x, y *Scalar) *Scalar { // s = x * y + 0 mod l fiatScalarMul(&s.s, &x.s, &y.s) return s } // Set...

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