(self, task_index, task_changed)
return (loss, stats)
Updates model weights/state and optimizer slots by running one step.

    Args:
      task_index (int): Index of the task to train on.
      task_changed (bool): Whether the state has changed since the last step.

    Returns:
      Tuple (loss, stats) with loss value from one step
      of training and stats, the current optimizer statistics.
Updates model weights/state and optimizer slots by running one step.
Updates model weights / state and optimizer slots by running one step .
def _run_one_step(self, task_index, task_changed):
    """Updates model weights/state and optimizer slots by running one step.

    Args:
      task_index (int): Index of the task to train on.
      task_changed (bool): Whether the state has changed since the last step.

    Returns:
      Tuple (loss, stats) with loss value from one step
      of training and stats, the current optimizer statistics.
    """
    step = self.step
    for callback in self._callbacks:
      if callback.call_at(step):
        callback.on_step_begin(step)

    learning_rate = self._tasks[task_index].learning_rate(step)
    batch = self._tasks[task_index].next_batch()
    rng = self.new_rng()
    trainer = self._trainer_per_task[task_index]
    if task_changed:
      # Re-replicate weights and state to synchronize them between tasks.
      self.update_weights_and_state(self._model.weights, self._model.state)

    (loss, stats) = trainer.one_step(
        batch, rng, step=step, learning_rate=learning_rate
    )

    for callback in self._callbacks:
      if callback.call_at(step):
        callback.on_step_end(step)

    return (loss, stats)
def _run_one_step ( self , task_index , task_changed ) : step = self . step for callback in self . _callbacks : if callback . call_at ( step ) : callback . on_step_begin ( step ) learning_rate = self . _tasks [ task_index ] . learning_rate ( step ) batch = self . _tasks [ task_index ] . next_batch ( ) rng = self . new_rng ( ) trainer = self . _trainer_per_task [ task_index ] if task_changed : # Re-replicate weights and state to synchronize them between tasks. self . update_weights_and_state ( self . _model . weights , self . _model . state ) ( loss , stats ) = trainer . one_step ( batch , rng , step = step , learning_rate = learning_rate ) for callback in self . _callbacks : if callback . call_at ( step ) : callback . on_step_end ( step ) return ( loss , stats )