class_docstr: Tfx runner on Airflow.
class_docstr_tok: ["Tfx","runner","on","Airflow","."]
docstr: Creates an instance of AirflowDagRunner.
  
  Args:
    config: Optional Airflow pipeline config for customizing the launching of
      each component.
docstr_tok: ["Creates","an","instance","of","AirflowDagRunner",".","Args",":","config",":","Optional","Airflow","pipeline","config","for","customizing","the","launching","of","each","component","."]
code: def __init__(
      self,
      config: Optional[
          Union[Dict[str, Any], AirflowPipelineConfig]
      ] = None,
  ):
      """Creates an instance of AirflowDagRunner.
  
      Args:
        config: Optional Airflow pipeline config for customizing the launching of
          each component.
      """
      if config and not isinstance(config, AirflowPipelineConfig):
          warnings.warn(
              "Pass config as a dict type is going to deprecated in 0.1.16. "
              "Use AirflowPipelineConfig type instead.",
              PendingDeprecationWarning,
          )
          config = AirflowPipelineConfig(airflow_dag_config=config)
      super().__init__(config)
  
imports: import os
  import typing
  from typing import Any, Dict, Optional, Union
  import warnings
  from airflow import models
  from tfx.dsl.components.base import base_component
  from tfx.orchestration import pipeline
  from tfx.orchestration import tfx_runner
  from tfx.orchestration.airflow import airflow_component
  from tfx.orchestration.config import config_utils
  from tfx.orchestration.config import pipeline_config
  from tfx.orchestration.data_types import RuntimeParameter
  from tfx.utils.json_utils import json
label_desc: Use image node_id 1 to create a new AirflowDagRunner object from inherited base classes: tfx_runner with example: obj = AirflowDagRunner(config)