class_docstr: Kubeflow pipelines RuntimeConfig builder.
class_docstr_tok: ["Kubeflow","pipelines","RuntimeConfig","builder","."]
docstr: Creates a RuntimeConfigBuilder object.
  
  Args:
    pipeline_info: a TFX pipeline info object, containing pipeline root info.
    parameter_values: mapping from runtime parameter names to its values.
docstr_tok: ["Creates","a","RuntimeConfigBuilder","object",".","Args",":","pipeline_info",":","a","TFX","pipeline","info","object",",","containing","pipeline","root","info",".","parameter_values",":","mapping","from","runtime","parameter","names","to","its","values","."]
code: def __init__(
      self,
      pipeline_info: data_types.PipelineInfo,
      parameter_values: Dict[str, Any],
  ):
      """Creates a RuntimeConfigBuilder object.
  
      Args:
        pipeline_info: a TFX pipeline info object, containing pipeline root info.
        parameter_values: mapping from runtime parameter names to its values.
      """
      self._pipeline_root = pipeline_info.pipeline_root
      self._parameter_values = parameter_values or {}
  
imports: import random
  import re
  import string
  import typing
  from typing import Any, Dict, List, Mapping, Optional, Union
  from absl import logging
  from kfp.pipeline_spec import pipeline_spec_pb2
  from tfx.dsl.components.base import base_node
  from tfx.dsl.placeholder import placeholder
  from tfx.orchestration import data_types
  from tfx.orchestration import pipeline
  from tfx.orchestration.kubeflow import utils
  from tfx.orchestration.kubeflow.v2 import compiler_utils
  from tfx.orchestration.kubeflow.v2 import parameter_utils
  from tfx.orchestration.kubeflow.v2 import step_builder
  from tfx.types import channel_utils
  from google.protobuf import json_format
label_desc: Use image node_id 1 to create a new RuntimeConfigBuilder object  with example: obj = RuntimeConfigBuilder(pipeline_info, parameter_values)