virtual Status InputLength(const char* input_name, int* length) = 0;
  virtual Status OutputLength(const char* output_name, int* length) = 0;

  // Set stack trace to be used for potential async error reporting.
  virtual void SetStackTrace(ManagedStackTrace stack_trace) = 0;

  virtual const tensorflow::AbstractOpAttrs* GetOpAttrs() const = 0;
  virtual void AddAttrs(const AbstractOpAttrs* op_attrs) = 0;

  options->stack_size = 0;
  options->guard_size = 0;
  options->numa_node = -1;
}

TF_Thread* TF_StartThread(const TF_ThreadOptions* options,
                          const char* thread_name, void (*work_func)(void*),
                          void* param) {
  ::tensorflow::ThreadOptions cc_options;
  cc_options.stack_size = options->stack_size;
  cc_options.guard_size = options->guard_size;

      * Try to build TF from sources by changing CPU optimization flags.
   
   *Please let us know if this helps.*
   
windows_comment: >
   From the stack trace it looks like you are hitting windows path length limit.
      * Try to disable path length limit on Windows 10.
        * Refer [disable path length limit instructions guide.](https://mspoweruser.com/ntfs-260-character-windows-10/)

  tensorflow::StackTracesMap stack_traces;
  for (const Node* n : fn_body->graph.nodes()) {
    stack_traces[n->name()] = n->GetStackTrace();
  }

  TF_Function* tf_function = new TF_Function();
  tf_function->record = new tensorflow::FunctionRecord(
      std::move(fdef), std::move(stack_traces), false);

  return tf_function;
}

                                                        TF_Status* status);

// Some TF ops need a step container to be set to limit the lifetime of some
// resources (mostly TensorArray and Stack, used in while loop gradients in
// graph mode). Calling this on a context tells it to start a step.
TF_CAPI_EXPORT extern void TFE_ContextStartStep(TFE_Context* ctx);

                                                     TF_Buffer* buf,
                                                     TF_Status* status);

// Get GraphDebugInfo containing stack traces mapping to node names
TF_CAPI_EXPORT extern void TFE_ContextGetGraphDebugInfo(
    TFE_Context* ctx, const char* function_name, TF_Buffer* buf,
    TF_Status* status);

// Extracts a TF_Function from the context.

          NodeDef* const node_def = graph_def.add_node();
          *node_def = node->def();
        }
      }
      *graph_def.mutable_library() = graph.flib_def().ToProto();
      if (flags::Global().more_stack_traces.value()) {
        *graph_def.mutable_debug_info() = graph.BuildDebugInfo();
      }
      session->graph->mu.unlock();
      status->status = session->session->Extend(std::move(graph_def));

tracking requests and bugs, please see
[TensorFlow Forum](https://discuss.tensorflow.org/) for general questions and
discussion, and please direct specific questions to
[Stack Overflow](https://stackoverflow.com/questions/tagged/tensorflow).**

The TensorFlow project strives to abide by generally accepted best practices in
open-source software development.

## Patching guidelines

  CancellationManager* cancellation_manager_ TF_GUARDED_BY(execution_mutex_);
  //   Outputs:
  std::vector<TensorHandlePtr> op_outputs_ TF_GUARDED_BY(execution_mutex_);
  // TF_Status is an incomplete type and so can't be stack allocated. To avoid
  // unnecessary allocations each Execute call, we keep one heap-allocated
  // version for the thread.
  StatusPtr status_ TF_GUARDED_BY(execution_mutex_);

  const std::string device_;

  std::vector<int64_t> tensor_stack;
  tensor_stack.reserve(target.size());
  for (auto t : target) {
    tensor_stack.push_back(t);
  }
  BackpropInitialState<BackwardFunction, TapeTensor> result;
  while (!tensor_stack.empty()) {
    int64_t tensor_id = tensor_stack.back();
    tensor_stack.pop_back();
    auto op_id_it = tensor_tape.find(tensor_id);