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 (str): an S3 uri where new input dataset is stored.
      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).
  
  Returns (dcit): a dictionary that can be used as args of
      sagemaker_session.transform method.
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code: def _get_transform_args(self, desc, inputs, name, volume_kms_key):
      """Format training args to pass in sagemaker_session.train.
  
      Args:
          desc (dict): the response from DescribeTrainingJob API.
          inputs (str): an S3 uri where new input dataset is stored.
          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).
  
      Returns (dcit): a dictionary that can be used as args of
          sagemaker_session.transform method.
      """
      transform_args = {}
      transform_args["job_name"] = name
      transform_args["model_name"] = desc["ModelName"]
      transform_args["output_config"] = desc["TransformOutput"]
      transform_args["resource_config"] = desc["TransformResources"]
      transform_args["data_processing"] = desc["DataProcessing"]
      transform_args["tags"] = []
      transform_args["strategy"] = None
      transform_args["max_concurrent_transforms"] = None
      transform_args["max_payload"] = None
      transform_args["env"] = None
      transform_args["experiment_config"] = None
  
      input_config = desc["TransformInput"]
      input_config["DataSource"]["S3DataSource"]["S3Uri"] = inputs
      transform_args["input_config"] = input_config
  
      if volume_kms_key is not None:
          transform_args["resource_config"][
              "VolumeKmsKeyId"
          ] = volume_kms_key
      if "BatchStrategy" in desc:
          transform_args["strategy"] = desc["BatchStrategy"]
      if "MaxConcurrentTransforms" in desc:
          transform_args["max_concurrent_transforms"] = desc[
              "MaxConcurrentTransforms"
          ]
      if "MaxPayloadInMB" in desc:
          transform_args["max_payload"] = desc["MaxPayloadInMB"]
      if "Environment" in desc:
          transform_args["env"] = desc["Environment"]
  
      return transform_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 6 for calling the CandidateEstimator obj's underlying member method code with example usage: obj._get_transform_args(desc, inputs, name, volume_kms_key) and returns: transform_args