def plot_lrs_for_config(configs: TrainingConfig, num_training_steps: int, start_lr: int = 100):
    """
    Plot the lr for a config.
    It's useful to visualize how the lr scheduler control the lr by the yaml file configs.
    Args:
        configs (`TrainingConfig`):
            The training config, which can be load from the yaml file.
        num_training_steps (`int`):
            Total training step number.
        start_lr (`int`):
            The start lr value.

    """
    model = nn.Linear(2, 1)
    optimizer = torch.optim.SGD(model.parameters(), lr=start_lr)
    lr_scheduler = _create_scheduler_from_config(configs, num_training_steps, optimizer)
    plot_lrs_for_scheduler(optimizer, lr_scheduler, num_training_steps)
Plot the lr for a config.
It's useful to visualize how the lr scheduler control the lr by the yaml file configs.
Args:
configs (`TrainingConfig`):
The training config, which can be load from the yaml file.
num_training_steps (`int`):
Total training step number.
start_lr (`int`):
The start lr value.
towhee/trainer/utils/plot_utils.py