parameters: (self, request)
return_statement: return self._run_start_response
docstring: Overrides on-run-start callback.
  
      Args:
        request: An instance of `OnRunStartRequest`.
  
      Returns:
        An instance of `OnRunStartResponse`.
docstring_summary: Overrides on-run-start callback.
docstring_tokens: Overrides on - run - start callback .
function: def on_run_start(self, request):
      """Overrides on-run-start callback.
  
      Args:
        request: An instance of `OnRunStartRequest`.
  
      Returns:
        An instance of `OnRunStartResponse`.
      """
      self._is_run_start = True
      self._update_run_calls_state(
          request.run_call_count, request.fetches, request.feed_dict,
          is_callable_runner=request.is_callable_runner)
  
      if self._active_tensor_filter:
        # If we are running until a filter passes, we just need to keep running
        # with the previous `OnRunStartResponse`.
        return self._active_tensor_filter_run_start_response
  
      self._exit_if_requested_by_user()
  
      if self._run_call_count > 1 and not self._skip_debug:
        if self._run_through_times > 0:
          # Just run through without debugging.
          return framework.OnRunStartResponse(
              framework.OnRunStartAction.NON_DEBUG_RUN, [])
        elif self._run_through_times == 0:
          # It is the run at which the run-end CLI will be launched: activate
          # debugging.
          return (self._run_start_response or
                  framework.OnRunStartResponse(
                      framework.OnRunStartAction.DEBUG_RUN,
                      self._get_run_debug_urls()))
  
      if self._run_start_response is None:
        self._prep_cli_for_run_start()
  
        self._run_start_response = self._launch_cli()
        if self._active_tensor_filter:
          self._active_tensor_filter_run_start_response = self._run_start_response
        if self._run_through_times > 1:
          self._run_through_times -= 1
  
      self._exit_if_requested_by_user()
      return self._run_start_response
function_tokens: def on_run_start ( self , request ) : self . _is_run_start = True self . _update_run_calls_state ( request . run_call_count , request . fetches , request . feed_dict , is_callable_runner = request . is_callable_runner ) if self . _active_tensor_filter : # If we are running until a filter passes, we just need to keep running # with the previous `OnRunStartResponse`. return self . _active_tensor_filter_run_start_response self . _exit_if_requested_by_user ( ) if self . _run_call_count > 1 and not self . _skip_debug : if self . _run_through_times > 0 : # Just run through without debugging. return framework . OnRunStartResponse ( framework . OnRunStartAction . NON_DEBUG_RUN , [ ] ) elif self . _run_through_times == 0 : # It is the run at which the run-end CLI will be launched: activate # debugging. return ( self . _run_start_response or framework . OnRunStartResponse ( framework . OnRunStartAction . DEBUG_RUN , self . _get_run_debug_urls ( ) ) ) if self . _run_start_response is None : self . _prep_cli_for_run_start ( ) self . _run_start_response = self . _launch_cli ( ) if self . _active_tensor_filter : self . _active_tensor_filter_run_start_response = self . _run_start_response if self . _run_through_times > 1 : self . _run_through_times -= 1 self . _exit_if_requested_by_user ( ) return self . _run_start_response