def get_optimizers_iterable(self, batch_idx=None):
    '\n        Generates an iterable with (idx, optimizer) for each optimizer.\n        '
    if (not self.trainer.optimizer_frequencies):
        return list(enumerate(self.trainer.optimizers))
    if (batch_idx is None):
        batch_idx = self.trainer.total_batch_idx
    optimizers_loop_length = self.optimizer_freq_cumsum[(- 1)]
    current_place_in_loop = (batch_idx % optimizers_loop_length)
    opt_idx = np.argmax((self.optimizer_freq_cumsum > current_place_in_loop))
    return [[opt_idx, self.trainer.optimizers[opt_idx]]]
Generates an iterable with (idx, optimizer) for each optimizer.
def get_optimizers_iterable(self, batch_idx=None):
    '\n        \n        '
    if (not self.trainer.optimizer_frequencies):
        return list(enumerate(self.trainer.optimizers))
    if (batch_idx is None):
        batch_idx = self.trainer.total_batch_idx
    optimizers_loop_length = self.optimizer_freq_cumsum[(- 1)]
    current_place_in_loop = (batch_idx % optimizers_loop_length)
    opt_idx = np.argmax((self.optimizer_freq_cumsum > current_place_in_loop))
    return [[opt_idx, self.trainer.optimizers[opt_idx]]]