code: def test_sam_semantic_segmentation_get_class_num_func(
      checkpoint_name,
  ):
      train_df, _, _ = get_file_df_multi_semantic_seg(need_test_gt=True)
  
      validation_metric = "iou"
      predictor = MultiModalPredictor(
          problem_type="semantic_segmentation",
          validation_metric=validation_metric,
          eval_metric=validation_metric,
          hyperparameters={
              "env.precision": 32,
              "model.sam.checkpoint_name": checkpoint_name,
              "optimization.loss_function": "mask2former_loss",
              "model.sam.num_mask_tokens": 10,
          },
          label="label",
      )
  
      get_class_num_func = (
          predictor._learner.get_semantic_segmentation_class_num
      )
      num_classes = (
          11  # the true number of classes within the provided data
      )
  
      # pd.DataFrame as input
      assert num_classes == get_class_num_func(train_df)
      # file path as input
      assert num_classes == get_class_num_func(
          train_df["label"][2]
      )  # tiny_trans10kcls12/train/masks_12/2492_mask.png
      # file directory path as input
      assert num_classes == get_class_num_func(
          os.path.dirname(train_df["label"][0])
      )
  
imports: import os
  import shutil
  import uuid
  import numpy
  import numpy.testing
  import pandas
  import pytest
  import torch
  from autogluon.core.utils.loaders import load_zip
  from autogluon.multimodal import MultiModalPredictor
label_desc: Use image node_id 11 for calling a global function with example usage: test_sam_semantic_segmentation_get_class_num_func(checkpoint_name) without return types