class_docstr: Base class for IR-based TFX runners.
class_docstr_tok: ["Base","class","for","IR-based","TFX","runners","."]
docstr: Runs a TFX pipeline on a specific platform.
  
  Args:
    pipeline: a pipeline_pb2.Pipeline instance representing a pipeline
      definition.
    run_options: Optional args for the run.
    **kwargs: extra orchestrator backend-specific keyword arguments.
  
  Returns:
    Optional platform-specific object.
docstr_tok: ["Runs","a","TFX","pipeline","on","a","specific","platform",".","Args",":","pipeline",":","a","pipeline_pb2.Pipeline","instance","representing","a","pipeline","definition",".","run_options",":","Optional","args","for","the","run",".","*","*","kwargs",":","extra","orchestrator","backend-specific","keyword","arguments",".","Returns",":","Optional","platform-specific","object","."]
code: def run_with_ir(
      self,
      pipeline: pipeline_pb2.Pipeline,
      run_options: Optional[pipeline_pb2.RunOptions] = None,
      **kwargs: Any,
  ) -> Optional[Any]:
      """Runs a TFX pipeline on a specific platform.
  
      Args:
        pipeline: a pipeline_pb2.Pipeline instance representing a pipeline
          definition.
        run_options: Optional args for the run.
        **kwargs: extra orchestrator backend-specific keyword arguments.
  
      Returns:
        Optional platform-specific object.
      """
      pass
  
file_path: tfx/tfx/orchestration/portable/tfx_runner.py
imports: import abc
  from typing import Any, Optional
  from tfx.dsl.compiler import compiler
  from tfx.dsl.components.base import base_component
  from tfx.orchestration import pipeline
  from tfx.proto.orchestration import pipeline_pb2
  from tfx.utils import doc_controls
label_desc: Use image node_id 1 for calling the IrBasedRunner obj's underlying member method code with example usage: obj.run_with_ir(pipeline, run_options) without return types