class_docstr: Abstract class interacting with CAIP CMLE job or Vertex CustomJob.
class_docstr_tok: ["Abstract","class","interacting","with","CAIP","CMLE","job","or","Vertex","CustomJob","."]
docstr: Get training args for runner._launch_aip_training.
  
  The training args contain the inputs/outputs/exec_properties to the
  tfx.scripts.run_executor module.
  
  Args:
    input_dict: Passthrough input dict for tfx.components.Trainer.executor.
    output_dict: Passthrough input dict for tfx.components.Trainer.executor.
    exec_properties: Passthrough input dict for
      tfx.components.Trainer.executor.
    executor_class_path: class path for TFX core default trainer.
    job_args: Training input argument for AI Platform training job.
    job_id: Job ID for AI Platform Training job. If not supplied,
      system-determined unique ID is given.
    job_labels: Labels for AI Platform training job.
  
  
  Returns:
    A dict containing the training arguments
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code: def create_training_job(
      self,
      input_dict,
      output_dict,
      exec_properties,
      executor_class_path,
      job_args,
      job_id,
      job_labels,
  ) -> Dict[str, Any]:
      """Get training args for runner._launch_aip_training.
  
      The training args contain the inputs/outputs/exec_properties to the
      tfx.scripts.run_executor module.
  
      Args:
        input_dict: Passthrough input dict for tfx.components.Trainer.executor.
        output_dict: Passthrough input dict for tfx.components.Trainer.executor.
        exec_properties: Passthrough input dict for
          tfx.components.Trainer.executor.
        executor_class_path: class path for TFX core default trainer.
        job_args: Training input argument for AI Platform training job.
        job_id: Job ID for AI Platform Training job. If not supplied,
          system-determined unique ID is given.
        job_labels: Labels for AI Platform training job.
  
  
      Returns:
        A dict containing the training arguments
      """
      pass
  
imports: import abc
  import datetime
  import json
  import random
  from typing import Any, Dict, List, Optional, Union
  from absl import logging
  from google.api_core import retry
  from google.cloud.aiplatform import gapic
  from google.cloud.aiplatform_v1.types.custom_job import CustomJob
  from google.cloud.aiplatform_v1.types.job_state import JobState
  from googleapiclient import discovery
  from tfx import types
  from tfx.types import artifact_utils
  from tfx.utils import telemetry_utils
  from tfx.utils import version_utils
label_desc: Use image node_id 3 for calling the AbstractJobClient obj's underlying member method code with example usage: obj.create_training_job(input_dict, output_dict, exec_properties, executor_class_path, job_args, job_id, job_labels) without return types