code: def test_sam_semantic_segmentation_trans10k_fit_eval_predict_save_load(
      checkpoint_name,
  ):
      # Multi-class semantic segmentation
      train_df, val_df, test_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",
          sample_data_path=train_df,
      )
  
      # Fit
      predictor.fit(
          train_data=train_df, tuning_data=val_df, time_limit=20
      )
  
      # Evaluation
      predictor.evaluate(test_df, metrics=[validation_metric])
  
      # Predict, save and load
      verify_predictor_save_load_for_semantic_seg(
          predictor, test_df, as_multiclass=True
      )
  
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 9 for calling a global function with example usage: test_sam_semantic_segmentation_trans10k_fit_eval_predict_save_load(checkpoint_name) without return types