class_docstr: A class for SageMaker AutoML Job Candidate
docstr: Extract candidate's step jobs name and type.
  
  Args:
      steps (list): a list of a candidate's step jobs.
  
  Returns (list): a list of extracted information about step jobs'
      name and type.
docstr_tok: ["Extract","candidate","'s","step","jobs","name","and","type",".","Args",":","steps","(","list",")",":","a","list","of","a","candidate","'s","step","jobs",".","Returns","(","list",")",":","a","list","of","extracted","information","about","step","jobs'","name","and","type","."]
code: def _process_steps(self, steps):
      """Extract candidate's step jobs name and type.
  
      Args:
          steps (list): a list of a candidate's step jobs.
  
      Returns (list): a list of extracted information about step jobs'
          name and type.
      """
      processed_steps = []
      for step in steps:
          step_name = step["CandidateStepName"]
          step_type = step["CandidateStepType"].split("::")[2]
          processed_steps.append({"name": step_name, "type": step_type})
      return processed_steps
  
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 7 for calling the CandidateEstimator obj's underlying member method code with example usage: obj._process_steps(steps) and returns: processed_steps