code: def test_model_predicted_label_config():
      label = "label"
      probability = "pr"
      probability_threshold = 0.2
      label_headers = ["success"]
      model_config = ModelPredictedLabelConfig(
          label=label,
          probability=probability,
          probability_threshold=probability_threshold,
          label_headers=label_headers,
      )
      pr_threshold, config = model_config.get_predictor_config()
      expected_config = {
          "label": label,
          "probability": probability,
          "label_headers": label_headers,
      }
      assert probability_threshold == pr_threshold
      assert expected_config == 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 15 for calling a global function with example usage: test_model_predicted_label_config() without return types