class_docstr: ClarifyCheckStep step for workflow.
docstr: Generates input and output parameters for the clarify processing job
  
  Args:
      analysis_config (dict): A clarify processing job analysis config
      baselining_processor (SageMakerClarifyProcessor): A SageMakerClarifyProcessor instance
  
  Returns:
      dict: with two ProcessingInput objects as the clarify processing job inputs and
          a ProcessingOutput object as the clarify processing job output parameter
code: def _generate_processing_job_parameters(
      self,
      analysis_config: dict,
      baselining_processor: SageMakerClarifyProcessor,
  ) -> dict:
      """Generates input and output parameters for the clarify processing job
  
      Args:
          analysis_config (dict): A clarify processing job analysis config
          baselining_processor (SageMakerClarifyProcessor): A SageMakerClarifyProcessor instance
  
      Returns:
          dict: with two ProcessingInput objects as the clarify processing job inputs and
              a ProcessingOutput object as the clarify processing job output parameter
      """
      data_config = self.clarify_check_config.data_config
      analysis_config["methods"]["report"] = {
          "name": "report",
          "title": "Analysis Report",
      }
  
      with tempfile.TemporaryDirectory() as tmpdirname:
          analysis_config_file = os.path.join(
              tmpdirname, "analysis_config.json"
          )
          with open(analysis_config_file, "w") as f:
              json.dump(analysis_config, f)
          s3_analysis_config_file = _upload_analysis_config(
              analysis_config_file,
              data_config.s3_analysis_config_output_path
              or data_config.s3_output_path,
              baselining_processor.sagemaker_session,
              self.clarify_check_config.kms_key,
          )
          config_input = ProcessingInput(
              input_name="analysis_config",
              source=s3_analysis_config_file,
              destination=SageMakerClarifyProcessor._CLARIFY_CONFIG_INPUT,
              s3_data_type="S3Prefix",
              s3_input_mode="File",
              s3_compression_type="None",
          )
          data_input = ProcessingInput(
              input_name="dataset",
              source=data_config.s3_data_input_path,
              destination=SageMakerClarifyProcessor._CLARIFY_DATA_INPUT,
              s3_data_type="S3Prefix",
              s3_input_mode="File",
              s3_data_distribution_type=data_config.s3_data_distribution_type,
              s3_compression_type=data_config.s3_compression_type,
          )
          result_output = ProcessingOutput(
              source=SageMakerClarifyProcessor._CLARIFY_OUTPUT,
              destination=data_config.s3_output_path,
              output_name="analysis_result",
              s3_upload_mode=ProcessingOutputHandler.get_s3_upload_mode(
                  analysis_config
              ),
          )
      return dict(
          config_input=config_input,
          data_input=data_input,
          result_output=result_output,
      )
  
imports: from __future__ import absolute_import
  import copy
  import json
  import os
  import tempfile
  from abc import ABC
  from typing import List, Union, Optional
  import attr
  from sagemaker import s3
  from sagemaker.clarify import DataConfig, BiasConfig, ModelConfig, ModelPredictedLabelConfig, SHAPConfig, ProcessingOutputHandler, _upload_analysis_config, SageMakerClarifyProcessor, _set
  from sagemaker.model_monitor import BiasAnalysisConfig, ExplainabilityAnalysisConfig
  from sagemaker.model_monitor.model_monitoring import _MODEL_MONITOR_S3_PATH
  from sagemaker.processing import ProcessingInput, ProcessingOutput, ProcessingJob
  from sagemaker.utils import name_from_base
  from sagemaker.workflow import is_pipeline_variable
  from sagemaker.workflow.entities import RequestType, PipelineVariable
  from sagemaker.workflow.properties import Properties
  from sagemaker.workflow.step_collections import StepCollection
  from sagemaker.workflow.steps import Step, StepTypeEnum, CacheConfig
  from sagemaker.workflow.check_job_config import CheckJobConfig
  from sagemaker.workflow.utilities import trim_request_dict
label_desc: Use image node_id 6 for calling the ClarifyCheckStep obj's underlying member method code with example usage: obj._generate_processing_job_parameters(analysis_config, baselining_processor) and returns: dict