code: def test_image_config():
      model_type = "IMAGE_CLASSIFICATION"
      num_segments = 2
      feature_extraction_method = "segmentation"
      segment_compactness = 10
      max_objects = 4
      iou_threshold = 0.5
      context = 1.0
      image_config = ImageConfig(
          model_type=model_type,
          num_segments=num_segments,
          feature_extraction_method=feature_extraction_method,
          segment_compactness=segment_compactness,
          max_objects=max_objects,
          iou_threshold=iou_threshold,
          context=context,
      )
      expected_config = {
          "model_type": model_type,
          "num_segments": num_segments,
          "feature_extraction_method": feature_extraction_method,
          "segment_compactness": segment_compactness,
          "max_objects": max_objects,
          "iou_threshold": iou_threshold,
          "context": context,
      }
  
      assert expected_config == image_config.get_image_config()
  
imports: from __future__ import absolute_import, print_function
  import copy
  import pytest
  from mock import MagicMock, Mock, patch
  from sagemaker import Processor, image_uris
  from sagemaker.clarify import BiasConfig, DataConfig, ModelConfig, ModelPredictedLabelConfig, PDPConfig, SageMakerClarifyProcessor, SHAPConfig, TextConfig, ImageConfig, _AnalysisConfigGenerator, DatasetType, ProcessingOutputHandler, SegmentationConfig
label_desc: Use image node_id 24 for calling a global function with example usage: test_image_config() without return types