class_docstr: Runtime configuration parameters specific to execution on Kubernetes.
class_docstr_tok: ["Runtime","configuration","parameters","specific","to","execution","on","Kubernetes","."]
docstr: Creates a KubernetesDagRunnerConfig object.
  
  Args:
    tfx_image: The TFX container image to use in the pipeline.
    supported_launcher_classes: Optional list of component launcher classes
      that are supported by the current pipeline. List sequence determines the
      order in which launchers are chosen for each component being run.
    **kwargs: keyword args for PipelineConfig.
docstr_tok: ["Creates","a","KubernetesDagRunnerConfig","object",".","Args",":","tfx_image",":","The","TFX","container","image","to","use","in","the","pipeline",".","supported_launcher_classes",":","Optional","list","of","component","launcher","classes","that","are","supported","by","the","current","pipeline",".","List","sequence","determines","the","order","in","which","launchers","are","chosen","for","each","component","being","run",".","*","*","kwargs",":","keyword","args","for","PipelineConfig","."]
code: def __init__(
      self,
      tfx_image: Optional[str] = None,
      supported_launcher_classes: Optional[
          List[Type[base_component_launcher.BaseComponentLauncher]]
      ] = None,
      **kwargs,
  ):
      """Creates a KubernetesDagRunnerConfig object.
  
      Args:
        tfx_image: The TFX container image to use in the pipeline.
        supported_launcher_classes: Optional list of component launcher classes
          that are supported by the current pipeline. List sequence determines the
          order in which launchers are chosen for each component being run.
        **kwargs: keyword args for PipelineConfig.
      """
      supported_launcher_classes = supported_launcher_classes or [
          in_process_component_launcher.InProcessComponentLauncher,
          kubernetes_component_launcher.KubernetesComponentLauncher,
      ]
      super().__init__(
          supported_launcher_classes=supported_launcher_classes,
          **kwargs,
      )
      self.tfx_image = tfx_image or _TFX_IMAGE
  
imports: import datetime
  import json
  from typing import List, Optional, Type
  from absl import logging
  from tfx.dsl.component.experimental import container_component
  from tfx.dsl.components.base import base_node
  from tfx.orchestration import data_types
  from tfx.orchestration import metadata
  from tfx.orchestration import pipeline
  from tfx.orchestration import tfx_runner
  from tfx.orchestration.config import base_component_config
  from tfx.orchestration.config import config_utils
  from tfx.orchestration.config import pipeline_config
  from tfx.orchestration.experimental.kubernetes import kubernetes_remote_runner
  from tfx.orchestration.experimental.kubernetes import node_wrapper
  from tfx.orchestration.launcher import base_component_launcher
  from tfx.orchestration.launcher import in_process_component_launcher
  from tfx.orchestration.launcher import kubernetes_component_launcher
  from tfx.utils import json_utils
  from tfx.utils import kube_utils
  from tfx.utils import name_utils
  from google.protobuf import json_format
  from ml_metadata.proto import metadata_store_pb2
label_desc: Use image node_id 1 to create a new KubernetesDagRunnerConfig object from inherited base classes: pipeline_config with example: obj = KubernetesDagRunnerConfig(tfx_image, supported_launcher_classes)