code: def get_mixup(
      model_config: DictConfig,
      mixup_config: DictConfig,
      num_classes: int,
  ):
      """
      Get the mixup state for loss function choice.
      Now the mixup can only support image data.
      And the problem type can not support Regression.
      Parameters
      ----------
      model_config
          The model configs to find image model for the necessity of mixup.
      mixup_config
          The mixup configs for mixup and cutmix.
      num_classes
          The number of classes in the task. Class <= 1 will cause faults.
  
      Returns
      -------
      The mixup is on or off.
      """
      model_active = False
      names = model_config.names
      if isinstance(names, str):
          names = [names]
      for model_name in names:
          permodel_config = getattr(model_config, model_name)
          if hasattr(permodel_config.data_types, IMAGE):
              model_active = True
              break
  
      mixup_active = False
      if mixup_config is not None and mixup_config.turn_on:
          mixup_active = (
              mixup_config.mixup_alpha > 0
              or mixup_config.cutmix_alpha > 0.0
              or mixup_config.cutmix_minmax is not None
          )
  
      mixup_state = (
          model_active
          & mixup_active
          & ((num_classes is not None) and (num_classes > 1))
      )
      mixup_fn = None
      if mixup_state:
          mixup_args = dict(
              mixup_alpha=mixup_config.mixup_alpha,
              cutmix_alpha=mixup_config.cutmix_alpha,
              cutmix_minmax=mixup_config.cutmix_minmax,
              prob=mixup_config.prob,
              switch_prob=mixup_config.switch_prob,
              mode=mixup_config.mode,
              label_smoothing=mixup_config.label_smoothing,
              num_classes=num_classes,
          )
          mixup_fn = MixupModule(**mixup_args)
      return mixup_state, mixup_fn
  
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 6 for calling a global function with example usage: get_mixup(model_config, mixup_config, num_classes) and returns: mixup_state,  mixup_fn