class_docstr: test lr scheduler plot.
code: def test_interpret_image_classification(self):
      self.mock_img = Image.new(mode="RGB", size=(20, 20))
      self.img_path = Path(cur_dir).parent / "mock_img.jpg"
      self.mock_img.save(self.img_path)
      val_transform = transforms.Compose(
          [
              transforms.Resize(100),
              transforms.CenterCrop(100),
              transforms.ToTensor(),
              transforms.Normalize(
                  mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
              ),
          ]
      )
      img = Image.open(self.img_path)
      model = resnet18(pretrained=True)
      interpret_image_classification(
          model, img, val_transform, "IntegratedGradients"
      )
      interpret_image_classification(
          model, img, val_transform, "Occlusion"
      )
      interpret_image_classification(
          model, img, val_transform, "GradientShap"
      )
      interpret_image_classification(
          model, img, val_transform, "Saliency"
      )
      self.img_path.unlink()
  
imports: import unittest
  import torch
  from pathlib import Path
  from torchvision import transforms
  from torchvision.models import resnet18
  from towhee.utils.pil_utils import PILImage
  from towhee.utils.matplotlib_utils import matplotlib
  from towhee.trainer.utils.plot_utils import image_folder_sample_show, image_folder_statistic, show_transform, plot_lrs_for_config, plot_lrs_for_scheduler, interpret_image_classification
  from towhee.trainer.training_config import TrainingConfig
  from torch import nn
  from torch.optim.lr_scheduler import StepLR
label_desc: Use image node_id 5 for calling the TestVisualizationLRScheduler obj's underlying member method code with example usage: obj.test_interpret_image_classification() without return types