class_docstr: Patches KubeflowDagRunner.run() with several customizations for CLI.
class_docstr_tok: ["Patches","KubeflowDagRunner.run","(",")","with","several","customizations","for","CLI","."]
docstr: Initialize KubeflowDagRunnerPatcher.
  
  Args:
    call_real_run: Specify KubeflowDagRunner.run() should be called.
    use_temporary_output_file: If True, we will override the default value of
      the pipeline package output path. Even if it is set to True, if users
      specified a path in KubeflowDagRunner then this option will be ignored.
    build_image_fn: If specified, call the function with the configured
      tfx_image in the pipeline. The result of the function will be
      substituted as a new tfx_image of the pipeline.
docstr_tok: ["Initialize","KubeflowDagRunnerPatcher",".","Args",":","call_real_run",":","Specify","KubeflowDagRunner.run","(",")","should","be","called",".","use_temporary_output_file",":","If","True",",","we","will","override","the","default","value","of","the","pipeline","package","output","path",".","Even","if","it","is","set","to","True",",","if","users","specified","a","path","in","KubeflowDagRunner","then","this","option","will","be","ignored",".","build_image_fn",":","If","specified",",","call","the","function","with","the","configured","tfx_image","in","the","pipeline",".","The","result","of","the","function","will","be","substituted","as","a","new","tfx_image","of","the","pipeline","."]
code: def __init__(
      self,
      call_real_run: bool,
      use_temporary_output_file: bool = False,
      build_image_fn: Optional[Callable[[str], str]] = None,
  ):
      """Initialize KubeflowDagRunnerPatcher.
  
      Args:
        call_real_run: Specify KubeflowDagRunner.run() should be called.
        use_temporary_output_file: If True, we will override the default value of
          the pipeline package output path. Even if it is set to True, if users
          specified a path in KubeflowDagRunner then this option will be ignored.
        build_image_fn: If specified, call the function with the configured
          tfx_image in the pipeline. The result of the function will be
          substituted as a new tfx_image of the pipeline.
      """
      super().__init__(call_real_run)
      self._build_image_fn = build_image_fn
      self._use_temporary_output_file = use_temporary_output_file
  
file_path: tfx/tfx/tools/cli/handler/kubeflow_dag_runner_patcher.py
imports: import os
  import tempfile
  import typing
  from typing import Any, Callable, MutableMapping, Optional, Type
  from tfx.orchestration import pipeline
  from tfx.orchestration import tfx_runner
  from tfx.orchestration.kubeflow import kubeflow_dag_runner
  from tfx.tools.cli.handler import dag_runner_patcher
label_desc: Use image node_id 1 to create a new KubeflowDagRunnerPatcher object from inherited base classes: dag_runner_patcher with example: obj = KubeflowDagRunnerPatcher(call_real_run, use_temporary_output_file, build_image_fn)