code: def plot_lrs_for_scheduler(
      optimizer: torch.optim.Optimizer,
      scheduler: "_LRScheduler",
      total_steps: int,
  ):
      """
      Plot the lr curve for a specified optimizer and scheduler instance.
      Args:
          optimizer (`Optimizer`):
              The optimizer contains the training parameters of a model.
          scheduler (`_LRScheduler`):
              The lr scheduler for an optimizer.
          total_steps (`int`):
              Total training step number.
  
      """
      if not is_matplotlib_available():
          trainer_log.warning("Matplotlib is not available.")
      from towhee.utils.matplotlib_utils import matplotlib
      import matplotlib.pylab as plt  # pylint: disable=import-outside-toplevel
  
      lrs = []
      for _ in range(total_steps):
          optimizer.step()
          lrs.append(optimizer.param_groups[0]["lr"])
          scheduler.step()
      plt.plot(range(total_steps), lrs)
      plt.show()
  
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 6 for calling a global function with example usage: plot_lrs_for_scheduler(optimizer, scheduler, total_steps) without return types