class_docstr: A class for SageMaker AutoML Job Candidate
docstr: Format training args to pass in sagemaker_session.train.
  
  Args:
      desc (dict): the response from DescribeTrainingJob API.
      inputs (list): a list of input data channels.
      name (str): the name of the step job.
      volume_kms_key (str): The KMS key id to encrypt data on the storage volume attached to
          the ML compute instance(s).
      encrypt_inter_container_traffic (bool): To encrypt all communications between ML compute
          instances in distributed training.
      vpc_config (dict): Specifies a VPC that jobs and hosted models have access to.
          Control access to and from training and model containers by configuring the VPC
  
  Returns (dcit): a dictionary that can be used as args of
      sagemaker_session.train method.
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code: def _get_train_args(
      self,
      desc,
      inputs,
      name,
      volume_kms_key,
      encrypt_inter_container_traffic,
      vpc_config,
  ):
      """Format training args to pass in sagemaker_session.train.
  
      Args:
          desc (dict): the response from DescribeTrainingJob API.
          inputs (list): a list of input data channels.
          name (str): the name of the step job.
          volume_kms_key (str): The KMS key id to encrypt data on the storage volume attached to
              the ML compute instance(s).
          encrypt_inter_container_traffic (bool): To encrypt all communications between ML compute
              instances in distributed training.
          vpc_config (dict): Specifies a VPC that jobs and hosted models have access to.
              Control access to and from training and model containers by configuring the VPC
  
      Returns (dcit): a dictionary that can be used as args of
          sagemaker_session.train method.
      """
      train_args = {
          "input_config": inputs,
          "job_name": name,
          "input_mode": desc["AlgorithmSpecification"][
              "TrainingInputMode"
          ],
          "role": desc["RoleArn"],
          "output_config": desc["OutputDataConfig"],
          "resource_config": desc["ResourceConfig"],
          "image_uri": desc["AlgorithmSpecification"]["TrainingImage"],
          "enable_network_isolation": desc["EnableNetworkIsolation"],
          "encrypt_inter_container_traffic": encrypt_inter_container_traffic,
          "use_spot_instances": desc["EnableManagedSpotTraining"],
          "hyperparameters": {},
          "stop_condition": {},
          "metric_definitions": None,
          "checkpoint_s3_uri": None,
          "checkpoint_local_path": None,
          "tags": [],
          "vpc_config": None,
      }
  
      if volume_kms_key is not None:
          train_args["resource_config"][
              "VolumeKmsKeyId"
          ] = volume_kms_key
      if "VpcConfig" in desc:
          train_args["vpc_config"] = desc["VpcConfig"]
      elif vpc_config is not None:
          train_args["vpc_config"] = vpc_config
      if "Hyperparameters" in desc:
          train_args["hyperparameters"] = desc["Hyperparameters"]
      if "CheckpointConfig" in desc:
          train_args["checkpoint_s3_uri"] = desc["CheckpointConfig"][
              "S3Uri"
          ]
          train_args["checkpoint_local_path"] = desc[
              "CheckpointConfig"
          ]["LocalPath"]
      if "StoppingCondition" in desc:
          train_args["stop_condition"] = desc["StoppingCondition"]
      return train_args
  
imports: from __future__ import absolute_import
  from six import string_types
  from sagemaker.config import TRAINING_JOB_VPC_CONFIG_PATH, TRAINING_JOB_VOLUME_KMS_KEY_ID_PATH, TRAINING_JOB_INTER_CONTAINER_ENCRYPTION_PATH
  from sagemaker.session import Session
  from sagemaker.job import _Job
  from sagemaker.utils import name_from_base, resolve_value_from_config
label_desc: Use image node_id 5 for calling the CandidateEstimator obj's underlying member method code with example usage: obj._get_train_args(desc, inputs, name, volume_kms_key, encrypt_inter_container_traffic, vpc_config) and returns: train_args