code: def predict_image_classification(
      model: nn.Module, input_: torch.Tensor
  ):
      """
      Predict using an image classification model.
      Args:
          model (`nn.Module`):
              Pytorch model.
          input_ (`Tensor`):
              Input image tensor.
      Returns:
          (`tuple`)
              Prediction score which max is 1, and label idx.
  
      """
      output = model(input_)
      output = F.softmax(output, dim=1)
      prediction_score, pred_label_idx = torch.topk(output, 1)
      if isinstance(pred_label_idx, torch.Tensor):
          pred_label_idx = pred_label_idx.squeeze().item()
      prediction_score = prediction_score.squeeze().detach().item()
      return prediction_score, pred_label_idx
  
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 9 for calling a global function with example usage: predict_image_classification(model, input_) and returns: prediction_score,  pred_label_idx