def run_Flatten(self, transform_node):
    step = self._add_step(TransformNames.FLATTEN,
                          transform_node.full_label, transform_node)
    inputs = []
    for one_input in transform_node.inputs:
      input_step = self._cache.get_pvalue(one_input)
      inputs.append(
          {'@type': 'OutputReference',
           PropertyNames.STEP_NAME: input_step.proto.name,
           PropertyNames.OUTPUT_NAME: input_step.get_output(one_input.tag)})
    step.add_property(PropertyNames.INPUTS, inputs)
    step.encoding = self._get_typehint_based_encoding(
        self._get_transform_type_hint(transform_node))
    step.add_property(
        PropertyNames.OUTPUT_INFO,
        [{PropertyNames.USER_NAME: (
            '%s.%s' % (transform_node.full_label, PropertyNames.OUT)),
          PropertyNames.ENCODING: step.encoding,
          PropertyNames.OUTPUT_NAME: PropertyNames.OUT}])
dataset/ETHPy150Open GoogleCloudPlatform/DataflowPythonSDK/google/cloud/dataflow/runners/dataflow_runner.py/DataflowPipelineRunner.run_Flatten/original