class_docstr: Analysis configuration for ModelExplainabilityMonitor.
class_docstr_tok: ["Analysis","configuration","for","ModelExplainabilityMonitor","."]
docstr: Creates an analysis config dictionary.
  
  Args:
      explainability_config (sagemaker.clarify.ExplainabilityConfig): Config object related
          to explainability configurations.
      model_config (sagemaker.clarify.ModelConfig): Config object related to bias
          configurations.
      headers (list[str]): A list of feature names (without label) of model/endpint input.
      label_headers (list[str]): List of headers, each for a predicted score in model output.
          It is used to beautify the analysis report by replacing placeholders like "label0".
code: def __init__(
      self,
      explainability_config,
      model_config,
      headers=None,
      label_headers=None,
  ):
      """Creates an analysis config dictionary.
  
      Args:
          explainability_config (sagemaker.clarify.ExplainabilityConfig): Config object related
              to explainability configurations.
          model_config (sagemaker.clarify.ModelConfig): Config object related to bias
              configurations.
          headers (list[str]): A list of feature names (without label) of model/endpint input.
          label_headers (list[str]): List of headers, each for a predicted score in model output.
              It is used to beautify the analysis report by replacing placeholders like "label0".
  
      """
      predictor_config = model_config.get_predictor_config()
      self.analysis_config = {
          "methods": explainability_config.get_explainability_config(),
          "predictor": predictor_config,
      }
      if headers is not None:
          self.analysis_config["headers"] = headers
      if label_headers is not None:
          predictor_config["label_headers"] = label_headers
  
imports: from __future__ import print_function, absolute_import
  import copy
  import json
  import logging
  import uuid
  from sagemaker.model_monitor import model_monitoring
  from sagemaker import image_uris, s3
  from sagemaker.session import Session
  from sagemaker.utils import name_from_base, format_tags
  from sagemaker.clarify import SageMakerClarifyProcessor, ModelPredictedLabelConfig
  from sagemaker.lineage._utils import get_resource_name_from_arn
label_desc: Use image node_id 1 to create a new ExplainabilityAnalysisConfig object  with example: obj = ExplainabilityAnalysisConfig(explainability_config, model_config, headers, label_headers)