class_docstr: Analysis configuration for ModelBiasMonitor.
class_docstr_tok: ["Analysis","configuration","for","ModelBiasMonitor","."]
docstr: Creates an analysis config dictionary.
  
  Args:
      bias_config (sagemaker.clarify.BiasConfig): Config object related to bias
          configurations.
      headers (list[str]): A list of column names in the input dataset.
      label (str): Target attribute for the model required by bias metrics. Specified as
          column name or index for CSV dataset, or as JMESPath expression for JSONLines.
code: def __init__(self, bias_config, headers=None, label=None):
      """Creates an analysis config dictionary.
  
      Args:
          bias_config (sagemaker.clarify.BiasConfig): Config object related to bias
              configurations.
          headers (list[str]): A list of column names in the input dataset.
          label (str): Target attribute for the model required by bias metrics. Specified as
              column name or index for CSV dataset, or as JMESPath expression for JSONLines.
      """
      self.analysis_config = bias_config.get_config()
      if headers is not None:
          self.analysis_config["headers"] = headers
      if label is not None:
          self.analysis_config["label"] = label
  
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 BiasAnalysisConfig object  with example: obj = BiasAnalysisConfig(bias_config, headers, label)