code: def dataset_processing_func(
      self,
      example: Dict[str, Any],
      data_keys: Dict[str, str],
      ref_keys: Optional[List[str]] = None,
  ) -> Dict[str, Any]:
      image = example[data_keys["image"]]
      example["pixel_values"] = (
          self.transforms(image.convert("RGB"))
          .to(torch.float32)
          .numpy()
      )
      return example
  
file_path: optimum/optimum/utils/preprocessing/image_classification.py
imports: from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union
  import torch
  from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor
  from transformers.image_processing_utils import VALID_SIZE_DICT_KEYS, BaseImageProcessor
  from ..None import logging
  from .base import TaskProcessor
label_desc: Use image node_id 2 for calling the ImageClassificationProcessing obj's underlying member method code with example usage: obj.dataset_processing_func(example, data_keys, ref_keys) and returns: example