class_docstr: A directed acyclic graph pipeline.
  
  This Pipeline can be given an arbitrary graph composed of Pipeline instances
  and will run all of those pipelines feeding outputs to inputs. See README.md
  for details.
  
  Use DAGPipeline to compose multiple smaller pipelines together.
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docstr: Returns a list or dict which matches the type signature of dependency.
  
  Args:
    dependency: DagInput, PipelineKey, Pipeline instance, or dictionary
        mapping names to those values.
    outputs: A database of computed unit outputs. A dictionary mapping
        Pipeline to list of objects.
  
  Returns:
    A list or dictionary of computed unit outputs which matches the type
    signature of the given dependency.
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code: def _get_outputs_as_signature(self, dependency, outputs):
      """Returns a list or dict which matches the type signature of dependency.
  
      Args:
        dependency: DagInput, PipelineKey, Pipeline instance, or dictionary
            mapping names to those values.
        outputs: A database of computed unit outputs. A dictionary mapping
            Pipeline to list of objects.
  
      Returns:
        A list or dictionary of computed unit outputs which matches the type
        signature of the given dependency.
      """
  
      def _get_outputs_for_key(unit_or_key, outputs):
          if isinstance(unit_or_key, pipeline.PipelineKey):
              if not outputs[unit_or_key.unit]:
                  # If there are no outputs, just return nothing.
                  return outputs[unit_or_key.unit]
              assert isinstance(outputs[unit_or_key.unit], dict)
              return outputs[unit_or_key.unit][unit_or_key.key]
          assert isinstance(unit_or_key, (pipeline.Pipeline, DagInput))
          return outputs[unit_or_key]
  
      if isinstance(dependency, dict):
          return dict(
              (name, _get_outputs_for_key(unit_or_key, outputs))
              for name, unit_or_key in dependency.items()
          )
      return _get_outputs_for_key(dependency, outputs)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import itertools
  from magenta.pipelines import pipeline
  import six
label_desc: Use image node_id 7 for calling the DAGPipeline obj's underlying member method code with example usage: obj._get_outputs_as_signature(dependency, outputs) and returns: _get_outputs_for_key, outputs, dict, outputs, outputs