Run({{func_feed, Int32Tensor(3)}}, func_op, 2 + 3);
  VerifyFDef(
      {"add_0", "scalar"}, M({{"feed1"}, {"feed2"}}), M({{"add"}}),
      {{"feed1", "add_0:0"}, {"feed2", "add_0:1"}, {"add_0:sum:0", "add"}},
      {{"^scalar", "add_0:2"}});
}

TEST_F(CApiFunctionTest, ControlDependencyOutsideOfBody) {
  /*
   *                  |  |    scalar
   *                  |  |    .
   *                  v  v   . <---- control dependency

  // Construct a parallel tensor consisting of the scalar values from `values`.
  template <typename DataType>
  std::unique_ptr<ParallelTensor> ScalarsFromSequence(
      absl::Span<const DataType> values, TFE_Context* context,
      TF_Status* status) const;

  // A parallel tensor with scalar integers numbering component devices.
  std::unique_ptr<ParallelTensor> DeviceIDs(TFE_Context* context,

@needs_pydanticv1
def test_union_scalar_list():
    # For coverage
    # TODO: there might not be a current valid code path that uses this, it would
    # potentially enable query parameters defined as both a scalar and a list
    # but that would require more refactors, also not sure it's really useful
    from fastapi._compat import is_pv1_scalar_field

    field_info = FieldInfo()
    field = ModelField(
        name="foo",

  const std::vector<int64_t>* shape;
  TF_ASSERT_OK(unknown_length_vector->Shape(&shape));
  EXPECT_THAT(*shape, ElementsAre(-1));

  TensorHandlePtr scalar = FloatTensorHandle(2., status.get());
  ASSERT_TRUE(TF_GetCode(status.get()) == TF_OK) << TF_Message(status.get());
  two_vector = VectorFloatTensorHandle({3., 4.}, status.get());

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// APIs specific to Eager modes
// -----------------------------------------------------------------------------

// Temporary APIs till we figure out how to create scalar valued Eager
// tensors and how to get value out of eager abstract tensors.
TF_AbstractTensor* TF_CreateAbstractTensorFromEagerTensor(TFE_TensorHandle* t,
                                                          TF_Status* s);

    url: https://github.com/Kong
  - login: codacy
    avatarUrl: https://avatars.githubusercontent.com/u/1834093?v=4
    url: https://github.com/codacy
  - login: scalar
    avatarUrl: https://avatars.githubusercontent.com/u/301879?v=4
    url: https://github.com/scalar
- - login: ObliviousAI
    avatarUrl: https://avatars.githubusercontent.com/u/65656077?v=4
    url: https://github.com/ObliviousAI
- - login: databento

// Abstract interface to a context.
//
// A context is responsible for creating key objects such as Tensors,
// TensorHandles & Operations.
class ImmediateExecutionContext : public AbstractContext {
 public:
  // Optimized scalar creation functions
  virtual AbstractTensorInterface* CreateInt64Scalar(int64_t value) = 0;
  virtual AbstractTensorInterface* CreateUint64Scalar(uint64 value) = 0;

// Platform-specific implementation to return an unused port. (This should used
// in tests only.)
TF_CAPI_EXPORT int TF_PickUnusedPortOrDie(void);

// Fast path method that makes constructing a single scalar tensor require less
// overhead and copies.
TF_CAPI_EXPORT extern TFE_TensorHandle* TFE_NewTensorHandleFromScalar(
    TF_DataType data_type, void* data, size_t len, TF_Status* status);

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.

//// tab | Python 3.10+

```Python hl_lines="16  21"
{!> ../../docs_src/response_model/tutorial001_01_py310.py!}
```

////

//// tab | Python 3.9+