code: def get_image_transforms(
      model_config: DictConfig,
      model_name: str,
      advanced_hyperparameters: Dict,
  ):
      """
      Get the image transforms of one image-related model.
      Use the transforms in advanced_hyperparameters with higher priority.
  
      Parameters
      ----------
      model_config
          Config of one model.
      model_name
          Name of one model.
      advanced_hyperparameters
          The advanced hyperparameters whose values are complex objects.
  
      Returns
      -------
      The image transforms used in training and validation.
      """
      train_transform_key = f"model.{model_name}.train_transforms"
      val_transform_key = f"model.{model_name}.val_transforms"
      if (
          advanced_hyperparameters
          and train_transform_key in advanced_hyperparameters
      ):
          train_transforms = advanced_hyperparameters[
              train_transform_key
          ]
      else:
          train_transforms = (
              model_config.train_transform_types
              if hasattr(model_config, "train_transform_types")
              else model_config.train_transforms
          )
          train_transforms = list(train_transforms)
  
      if (
          advanced_hyperparameters
          and val_transform_key in advanced_hyperparameters
      ):
          val_transforms = advanced_hyperparameters[val_transform_key]
      else:
          val_transforms = (
              model_config.val_transform_types
              if hasattr(model_config, "val_transform_types")
              else model_config.val_transforms
          )
          val_transforms = list(val_transforms)
  
      return train_transforms, val_transforms
  
imports: import copy
  import logging
  import os
  import warnings
  from typing import Dict, List, Optional, Tuple, Union
  import pandas
  from omegaconf import DictConfig, OmegaConf
  from torch import nn
  from autogluon.core.utils import default_holdout_frac, generate_train_test_split_combined
  from autogluon.core.utils.loaders import load_pd
  from ..constants import BINARY, CATEGORICAL, DEFAULT_SHOT, DOCUMENT, FEW_SHOT, IMAGE, IMAGE_PATH, LABEL, MMLAB_MODELS, MULTICLASS, NER, NER_ANNOTATION, NER_TEXT, NUMERICAL, OVD, REGRESSION, ROIS, SAM, SEMANTIC_SEGMENTATION_IMG, TEXT, TEXT_NER
  from ..data import CategoricalProcessor, DocumentProcessor, ImageProcessor, LabelProcessor, MixupModule, MMDetProcessor, MMOcrProcessor, MultiModalFeaturePreprocessor, NerLabelEncoder, NerProcessor, NumericalProcessor, OVDProcessor, SemanticSegImageProcessor, TextProcessor
  from ..data.infer_types import is_image_column
label_desc: Use image node_id 12 for calling a global function with example usage: get_image_transforms(model_config, model_name, advanced_hyperparameters) and returns: train_transforms,  val_transforms