

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)
        input_step.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/VarMisuse@54/85 `inputs`->`input_step`