class_docstr: Wrap node as beam DoFn.
class_docstr_tok: ["Wrap","node","as","beam","DoFn","."]
docstr: Initializes the PipelineNodeAsDoFn.
  
  Args:
    pipeline_node: The specification of the node that this launcher lauches.
    mlmd_connection_config: ML metadata connection config.
    pipeline_info: The information of the pipeline that this node runs in.
    pipeline_runtime_spec: The runtime information of the pipeline that this
      node runs in.
    executor_spec: Specification for the executor of the node. This is
      expected for all nodes. This will be used to determine the specific
      ExecutorOperator class to be used to execute and will be passed into
      ExecutorOperator.
    custom_driver_spec: Specification for custom driver. This is expected only
      for advanced use cases.
    deployment_config: Deployment Config for the pipeline.
    pipeline: Optional for full run, required for partial run.
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code: def __init__(
      self,
      pipeline_node: pipeline_pb2.PipelineNode,
      mlmd_connection_config: metadata.ConnectionConfigType,
      pipeline_info: pipeline_pb2.PipelineInfo,
      pipeline_runtime_spec: pipeline_pb2.PipelineRuntimeSpec,
      executor_spec: Optional[message.Message],
      custom_driver_spec: Optional[message.Message],
      deployment_config: Optional[message.Message],
      pipeline: Optional[pipeline_pb2.Pipeline],
  ):
      """Initializes the PipelineNodeAsDoFn.
  
      Args:
        pipeline_node: The specification of the node that this launcher lauches.
        mlmd_connection_config: ML metadata connection config.
        pipeline_info: The information of the pipeline that this node runs in.
        pipeline_runtime_spec: The runtime information of the pipeline that this
          node runs in.
        executor_spec: Specification for the executor of the node. This is
          expected for all nodes. This will be used to determine the specific
          ExecutorOperator class to be used to execute and will be passed into
          ExecutorOperator.
        custom_driver_spec: Specification for custom driver. This is expected only
          for advanced use cases.
        deployment_config: Deployment Config for the pipeline.
        pipeline: Optional for full run, required for partial run.
      """
      self._pipeline_node = pipeline_node
      self._mlmd_connection_config = mlmd_connection_config
      self._pipeline_info = pipeline_info
      self._pipeline_runtime_spec = pipeline_runtime_spec
      self._executor_spec = executor_spec
      self._custom_driver_spec = custom_driver_spec
      self._node_id = pipeline_node.node_info.id
      self._deployment_config = deployment_config
      self._pipeline = pipeline
  
imports: import datetime
  from typing import Any, Iterable, List, Optional
  from absl import logging
  import apache_beam
  from tfx.dsl.compiler import constants
  from tfx.orchestration import metadata
  from tfx.orchestration.beam.legacy import beam_dag_runner
  from tfx.orchestration.config import pipeline_config
  from tfx.orchestration.local import runner_utils
  from tfx.orchestration.portable import launcher
  from tfx.orchestration.portable import partial_run_utils
  from tfx.orchestration.portable import runtime_parameter_utils
  from tfx.orchestration.portable import tfx_runner
  from tfx.proto.orchestration import local_deployment_config_pb2
  from tfx.proto.orchestration import pipeline_pb2
  from tfx.utils import telemetry_utils
  from google.protobuf import any_pb2
  from google.protobuf import message
label_desc: Use image node_id 1 to create a new PipelineNodeAsDoFn object from inherited base classes: beam with example: obj = PipelineNodeAsDoFn(pipeline_node, mlmd_connection_config, pipeline_info, pipeline_runtime_spec, executor_spec, custom_driver_spec, deployment_config, pipeline)