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<a href="https://github.com/scalar/scalar/?utm_source=fastapi&utm_medium=website&utm_campaign=main-badge" target="_blank" title="Scalar: Beautiful Open-Source API References from Swagger/OpenAPI files"><img src="https://fastapi.tiangolo.com/img/sponsors/scalar.svg"></a>

&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...

// 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);

&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.

      found_shape = true;
    }
  }
  return found_dtype && found_shape;
}

bool IsScalarConst(const tensorflow::NodeDef& node_def, int v) {
  if (node_def.op() != "Const" || node_def.name() != "scalar") {
    return false;
  }
  bool found_dtype = false;
  bool found_value = false;
  for (const auto& attr : node_def.attr()) {
    if (attr.first == "dtype") {

*   For C++ API users: `IsLegacyScalar` and `IsLegacyVector` are now gone from
    `TensorShapeUtils` since TensorFlow is scalar strict within Google (for
    example, the shape argument to `tf.reshape` can't be a scalar anymore). The
    open source release was already scalar strict, so outside Google `IsScalar`
    and `IsVector` are exact replacements.
*   The following files are being removed from `tensorflow/core/public/`:

    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()),

// into `dims`. `dims` must be an array large enough to hold `num_dims`
// entries (e.g., the return value of TF_GraphGetTensorNumDims).
//
// If the number of dimensions in the shape is unknown or the shape is
// a scalar, `dims` will remain untouched. Otherwise, each element of
// `dims` will be set corresponding to the size of the dimension. An
// unknown dimension is represented by `-1`.
//
// Returns an error into `status` if: