def _training_steps(self):
    '\n        Returns a generator over training steps (i.e. parameters update).\n        '
    for (i, loss) in enumerate(self._accumulate_next_gradients()):
        if ((i % self.args.gradient_accumulation_steps) == 0):
            self._apply_gradients()
            (yield loss)
Returns a generator over training steps (i.e. parameters update).
def _training_steps(self):
    '\n        \n        '
    for (i, loss) in enumerate(self._accumulate_next_gradients()):
        if ((i % self.args.gradient_accumulation_steps) == 0):
            self._apply_gradients()
            (yield loss)