code: def test_analysis_config_generator_for_bias(
      data_config, data_bias_config, model_config
  ):
      model_predicted_label_config = ModelPredictedLabelConfig(
          probability="pr",
          label_headers=["success"],
      )
      actual = _AnalysisConfigGenerator.bias(
          data_config,
          data_bias_config,
          model_config,
          model_predicted_label_config,
          pre_training_methods="all",
          post_training_methods="all",
      )
      expected = {
          "dataset_type": "text/csv",
          "facet": [{"name_or_index": "F1"}],
          "group_variable": "F2",
          "headers": ["Label", "F1", "F2", "F3", "F4"],
          "joinsource_name_or_index": "F4",
          "label": "Label",
          "label_values_or_threshold": [1],
          "methods": {
              "report": {"name": "report", "title": "Analysis Report"},
              "post_training_bias": {"methods": "all"},
              "pre_training_bias": {"methods": "all"},
          },
          "predictor": {
              "initial_instance_count": 1,
              "instance_type": "ml.c5.xlarge",
              "label_headers": ["success"],
              "model_name": "xgboost-model",
              "probability": "pr",
          },
      }
      assert actual == expected
  
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 54 for calling a global function with example usage: test_analysis_config_generator_for_bias(data_config, data_bias_config, model_config) without return types