code: 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
      )
  
imports: from typing import Optional, List, Any, Tuple
  from torchvision import transforms
  from torchvision.io import read_image
  from pathlib import Path
  from torch import nn
  from towhee.utils.pil_utils import PILImage
  from towhee.trainer.optimization.optimization import get_scheduler, get_warmup_steps
  from towhee.trainer.utils.trainer_utils import _construct_scheduler_from_config
  from towhee.trainer.training_config import TrainingConfig
  from towhee.trainer.utils.file_utils import is_captum_available, is_matplotlib_available
  from towhee.utils.log import trainer_log
  import torch
  import torch.nn.functional
  import numpy
  import random
  import os
  import copy
label_desc: Use image node_id 8 for calling a global function with example usage: plot_lrs_for_config(configs, num_training_steps, start_lr) without return types