url: https://github.com/cryptapi
  - 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

    ASSERT_TRUE(TF_GetCode(status.get()) == TF_OK) << TF_Message(status.get());
    delete to_delete;
  };
  std::unique_ptr<Variable, decltype(variable_deleter)> variable(
      Variable::Create(context, TF_FLOAT, /* Scalar */ {}, 0, device_name,
                       status.get()),
      variable_deleter);
  ASSERT_TRUE(TF_GetCode(status.get()) == TF_OK) << TF_Message(status.get());

// 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);

// 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;

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.

=== "Python 3.10+"

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

=== "Python 3.9+"

      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") {

    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:

*   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/`:

message HorizontalPodAutoscalerSpec {
  // reference to scaled resource; horizontal pod autoscaler will learn the current resource consumption
  // and will set the desired number of pods by using its Scale subresource.
  optional CrossVersionObjectReference scaleTargetRef = 1;

  // minReplicas is the lower limit for the number of replicas to which the autoscaler
  // can scale down.  It defaults to 1 pod.  minReplicas is allowed to be 0 if the