F release() {
    released_ = true;
    return std::move(f_);
  }

  bool is_released() const { return released_; }

 private:
  static_assert(!std::is_reference<F>::value, "F must not be a reference");

  bool released_ = false;
  F f_;
};

template <int&... ExplicitParameterBarrier, typename F,
          typename DecayF = typename std::decay<F>::type>

  ~GraphFunction() override;

  // GraphFunction maybe stay alive for the duration of the returned
  // FunctionDef.
  Status GetFunctionDef(const FunctionDef** fdef) override;

  // Returns a shared reference to the wrapped function.
  absl::StatusOr<core::RefCountPtr<FunctionRecord>> GetFunctionRecord()
      override {
    return func_record_.GetNewRef();
  }

  // For LLVM style RTTI.

TF_CAPI_EXPORT extern int TFE_OpGetFlatInputCount(const TFE_Op* op,
                                                  TF_Status* status);
// Returns a borrowed reference to one of `op`'s inputs. Use
// `TFE_TensorHandleCopySharingTensor` to make a new reference.
TF_CAPI_EXPORT extern TFE_TensorHandle* TFE_OpGetFlatInput(const TFE_Op* op,
                                                           int index,

      dynamic_addr(symbol) = static_addr(symbol) + load_addr
 
-     The choice of symbol is arbitrary. The static address we obtain
-     by constructing a non GOT reference to the symbol, the dynamic
-     address of the symbol we compute using adrp/add to compute the
-     symbol's address relative to the PC. */
-
-  ElfW(Addr) static_addr;
-  ElfW(Addr) dynamic_addr;
-

  let assignee_logins = assignees.map(x => x.login);
  assignee_logins.push(pr_resp.data.user.login);
  console.log(assignee_logins);
  // Create an new GH Issue and reference the Original PR
  const resp = await github.rest.issues.create({
    owner,
    repo,
    assignees: assignee_logins,
    title: `Issue created for Rollback of PR #${pr_number}: ${pr_title}`,

}

namespace {

// Helper method that creates a shape handle for a shape described by dims.
tensorflow::shape_inference::ShapeHandle ShapeHandleFromDims(
    tensorflow::shape_inference::InferenceContext* ic, int num_dims,
    const int64_t* dims) {
  if (num_dims != -1) {
    std::vector<tensorflow::shape_inference::DimensionHandle> dim_vec;
    dim_vec.reserve(num_dims);
    for (int i = 0; i < num_dims; ++i) {

#
# Common setup for all TF scripts.
#
# Make as FEW changes to this file as possible. It should not contain utility
# functions (except for tfrun); use dedicated scripts instead and reference them
# specifically. Use your best judgment to keep the scripts in this directory
# lean and easy to follow. When in doubt, remember that for CI scripts, "keep it
# simple" is MUCH more important than "don't repeat yourself."

//   example, one cannot use the output of "switch" node as input.
// - `inputs` and `outputs` cannot have reference types. Reference types are
//   not exposed through C API and are being replaced with Resources. We support
//   reference types inside function's body to support legacy code. Do not
//   use them in new code.
// - Every node in the function's body must have all of its inputs (including

      const auto& iter = input_nodes.find(node);
      if (iter == input_nodes.end()) {
        // This node is not referenced in inputs. Add it to the body.
        body_nodes->push_back(node);
      } else {
        // This node is referenced in inputs. Currently, we place an
        // artificial restriction and require that when num_opers=-1, such
        // nodes must have a single output.

**`Documentation`** |
------------------- |
[![Documentation](https://img.shields.io/badge/api-reference-blue.svg)](https://www.tensorflow.org/api_docs/) |

[TensorFlow](https://www.tensorflow.org/) is an end-to-end open source platform
for machine learning. It has a comprehensive, flexible ecosystem of