class_docstr: A class for handling eager execution.
  
  The default behavior for asynchronous execution is to serialize all ops on
  a single thread. Having different `Executor` objects in different threads
  enables executing ops asynchronously in parallel:
  
  ```python
  def thread_function():
    executor = executor.Executor(enable_async=True):
    context.set_executor(executor)
  
  a = threading.Thread(target=thread_function)
  a.start()
  b = threading.Thread(target=thread_function)
  b.start()
  ```
docstr: Clears errors raised in this executor during execution.
code: def clear_error(self):
      """Clears errors raised in this executor during execution."""
      pywrap_tfe.TFE_ExecutorClearError(self._handle)
  
file_path: tensorflow-upstream/tensorflow/python/eager/executor.py
imports: from tensorflow.python import pywrap_tfe
label_desc: Use image node_id 6 for calling the Executor obj's underlying member method code with example usage: obj.clear_error() without return types