class_docstr: Handle creating, starting and waiting for Amazon SageMaker jobs to finish.
  
  This class shouldn't be directly instantiated.
  
  Subclasses must define a way to create, start and wait for an Amazon
  SageMaker job.
class_docstr_tok: ["Handle","creating",",","starting","and","waiting","for","Amazon","SageMaker","jobs","to","finish",".","This","class","should","n't","be","directly","instantiated",".","Subclasses","must","define","a","way","to","create",",","start","and","wait","for","an","Amazon","SageMaker","job","."]
docstr: Placeholder docstring
docstr_tok: ["Placeholder","docstring"]
code: def _load_config(
      inputs, estimator, expand_role=True, validate_uri=True
  ):
      """Placeholder docstring"""
      input_config = _Job._format_inputs_to_input_config(
          inputs, validate_uri
      )
      role = (
          estimator.sagemaker_session.expand_role(estimator.role)
          if (expand_role and not is_pipeline_variable(estimator.role))
          else estimator.role
      )
      output_config = _Job._prepare_output_config(
          estimator.output_path,
          estimator.output_kms_key,
          disable_output_compression=estimator.disable_output_compression,
      )
      resource_config = _Job._prepare_resource_config(
          estimator.instance_count,
          estimator.instance_type,
          estimator.instance_groups,
          estimator.volume_size,
          estimator.volume_kms_key,
          estimator.keep_alive_period_in_seconds,
      )
      stop_condition = _Job._prepare_stop_condition(
          estimator.max_run, estimator.max_wait
      )
      vpc_config = estimator.get_vpc_config()
  
      model_channel = _Job._prepare_channel(
          input_config,
          estimator.model_uri,
          estimator.model_channel_name,
          validate_uri,
          content_type="application/x-sagemaker-model",
          input_mode="File",
      )
      if model_channel:
          input_config = [] if input_config is None else input_config
          input_config.append(model_channel)
  
      if estimator.enable_network_isolation():
          code_channel = _Job._prepare_channel(
              input_config,
              estimator.code_uri,
              estimator.code_channel_name,
              validate_uri,
          )
  
          if code_channel:
              input_config = (
                  [] if input_config is None else input_config
              )
              input_config.append(code_channel)
  
      return {
          "input_config": input_config,
          "role": role,
          "output_config": output_config,
          "resource_config": resource_config,
          "stop_condition": stop_condition,
          "vpc_config": vpc_config,
      }
  
imports: from __future__ import absolute_import
  from abc import abstractmethod
  from six import string_types
  from sagemaker.inputs import FileSystemInput, TrainingInput
  from sagemaker.local import file_input
  from sagemaker.workflow import is_pipeline_variable
label_desc: Use image node_id 6 for calling the _Job obj's underlying member method code with example usage: obj._load_config(inputs, estimator, expand_role, validate_uri) and returns: dict