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

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  int num_dims_out = TF_NumDims(model_out_tensor);
  TF_DeleteTensor(model_out_tensor);

  // If the output is a scalar, then return the scalar output
  if (num_dims_out == 0) {
    outputs[0] = model_out.release();
    return absl::OkStatus();
  }

  // Else, reduce sum the output to get a scalar

  // Will sum all dimensions, so get a Tensor containing [0,...,num_dims_out-1].
  AbstractTensorHandlePtr sum_dims;
  {

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Si vous le déclarez tel quel, comme c'est une valeur [scalaire](https://docs.github.com/fr/graphql/reference/scalars), **FastAPI** supposera qu'il s'agit d'un paramètre de requête (`Query`).

Mais vous pouvez indiquer à **FastAPI** de la traiter comme une variable de body en utilisant `Body` :
//// tab | Python 3.10+

  // 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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