class_docstr: Check job config for QualityCheckStep and ClarifyCheckStep.
docstr: Constructs a CheckJobConfig instance.
  
  Args:
      role (str): An AWS IAM role. The Amazon SageMaker jobs use this role.
      instance_count (int): The number of instances to run the jobs with (default: 1).
      instance_type (str): Type of EC2 instance to use for the job
          (default: 'ml.m5.xlarge').
      volume_size_in_gb (int): Size in GB of the EBS volume
          to use for storing data during processing (default: 30).
      volume_kms_key (str): A KMS key for the processing volume (default: None).
      output_kms_key (str): The KMS key id for the job's outputs (default: None).
      max_runtime_in_seconds (int): Timeout in seconds. After this amount of
          time, Amazon SageMaker terminates the job regardless of its current status.
          Default: 3600 if not specified
      base_job_name (str): Prefix for the job name. If not specified,
          a default name is generated based on the training image name and
          current timestamp (default: None).
      sagemaker_session (sagemaker.session.Session): Session object which
          manages interactions with Amazon SageMaker APIs and any other
          AWS services needed (default: None). If not specified, one is
          created using the default AWS configuration chain.
      env (dict): Environment variables to be passed to the job (default: None).
      tags (Optional[Tags]): List of tags to be passed to the job (default: None).
      network_config (sagemaker.network.NetworkConfig): A NetworkConfig
          object that configures network isolation, encryption of
          inter-container traffic, security group IDs, and subnets (default: None).
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code: def __init__(
      self,
      role,
      instance_count=1,
      instance_type="ml.m5.xlarge",
      volume_size_in_gb=30,
      volume_kms_key=None,
      output_kms_key=None,
      max_runtime_in_seconds=None,
      base_job_name=None,
      sagemaker_session=None,
      env=None,
      tags=None,
      network_config=None,
  ):
      """Constructs a CheckJobConfig instance.
  
      Args:
          role (str): An AWS IAM role. The Amazon SageMaker jobs use this role.
          instance_count (int): The number of instances to run the jobs with (default: 1).
          instance_type (str): Type of EC2 instance to use for the job
              (default: 'ml.m5.xlarge').
          volume_size_in_gb (int): Size in GB of the EBS volume
              to use for storing data during processing (default: 30).
          volume_kms_key (str): A KMS key for the processing volume (default: None).
          output_kms_key (str): The KMS key id for the job's outputs (default: None).
          max_runtime_in_seconds (int): Timeout in seconds. After this amount of
              time, Amazon SageMaker terminates the job regardless of its current status.
              Default: 3600 if not specified
          base_job_name (str): Prefix for the job name. If not specified,
              a default name is generated based on the training image name and
              current timestamp (default: None).
          sagemaker_session (sagemaker.session.Session): Session object which
              manages interactions with Amazon SageMaker APIs and any other
              AWS services needed (default: None). If not specified, one is
              created using the default AWS configuration chain.
          env (dict): Environment variables to be passed to the job (default: None).
          tags (Optional[Tags]): List of tags to be passed to the job (default: None).
          network_config (sagemaker.network.NetworkConfig): A NetworkConfig
              object that configures network isolation, encryption of
              inter-container traffic, security group IDs, and subnets (default: None).
  
      """
      self.role = role
      self.instance_count = instance_count
      self.instance_type = instance_type
      self.volume_size_in_gb = volume_size_in_gb
      self.volume_kms_key = volume_kms_key
      self.output_kms_key = output_kms_key
      self.max_runtime_in_seconds = max_runtime_in_seconds
      self.base_job_name = base_job_name
      self.sagemaker_session = sagemaker_session or Session()
      self.env = env
      self.tags = format_tags(tags)
      self.network_config = network_config
  
file_path: sagemaker-python-sdk/src/sagemaker/workflow/check_job_config.py
imports: from __future__ import absolute_import
  import logging
  from typing import Optional
  from sagemaker import Session
  from sagemaker.model_monitor import ModelMonitor, DefaultModelMonitor, ModelQualityMonitor, ModelBiasMonitor, ModelExplainabilityMonitor
  from sagemaker.utils import format_tags
label_desc: Use image node_id 1 to create a new CheckJobConfig object  with example: obj = CheckJobConfig(role, instance_count, instance_type, volume_size_in_gb, volume_kms_key, output_kms_key, max_runtime_in_seconds, base_job_name, sagemaker_session, env, tags, network_config)