code: def __init__(self, folder_path, transform=None):
      self.folder_path = folder_path
      self.transform = transform
      # Use glob to find all image files in folder_path
      image_paths = []
      for ext in _IMG_EXTENSIONS + [x.upper() for x in _IMG_EXTENSIONS]:
          search_pattern = os.path.join(folder_path, f"*.{ext}")
          image_paths += glob.glob(search_pattern)
      self.image_paths = image_paths
  
imports: import glob
  import os
  import torch
  from omegaconf.omegaconf import OmegaConf, open_dict
  from PIL import Image
  from pytorch_lightning import Trainer
  from pytorch_lightning.plugins.environments import TorchElasticEnvironment
  from torch.utils.data import DataLoader, Dataset
  from nemo.collections.nlp.parts.nlp_overrides import NLPDDPStrategy, NLPSaveRestoreConnector
  from nemo.collections.nlp.parts.utils_funcs import torch_dtype_from_precision
  from nemo.collections.vision.data.imagenet_classnames import imagenet_classnames
  from nemo.collections.vision.data.megatron.vit_dataset import ClassificationTransform
  from nemo.collections.vision.models.megatron_vit_classification_models import MegatronVitClassificationModel
  from nemo.core.config import hydra_runner
  from nemo.utils import logging
  from nemo.utils.get_rank import is_global_rank_zero
label_desc: Use image node_id 1 to create a new ImageFolderDataset object from inherited base classes: Dataset with example: obj = ImageFolderDataset(folder_path, transform)