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: Runs the DAG on the given input.
  
  All pipelines in the DAG will run.
  
  Args:
    input_object: Any object. The required type depends on implementation.
  
  Returns:
    A dictionary mapping output names to lists of objects. The object types
    depend on implementation. Each output name corresponds to an output
    collection. See get_output_names method.
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code: def transform(self, input_object):
      """Runs the DAG on the given input.
  
      All pipelines in the DAG will run.
  
      Args:
        input_object: Any object. The required type depends on implementation.
  
      Returns:
        A dictionary mapping output names to lists of objects. The object types
        depend on implementation. Each output name corresponds to an output
        collection. See get_output_names method.
      """
  
      def stats_accumulator(unit, unit_inputs, cumulative_stats):
          for single_input in unit_inputs:
              results_ = unit.transform(single_input)
              stats = unit.get_stats()
              cumulative_stats.extend(stats)
              yield results_
  
      stats = []
      results = {self.input: [input_object]}
      for unit in self.call_list[1:]:
          # Compute transformation.
  
          if isinstance(unit, DagOutput):
              unit_outputs = self._get_outputs_as_signature(
                  self.dag[unit], results
              )
          else:
              unit_inputs = self._get_inputs_for_unit(unit, results)
              if not unit_inputs:
                  # If this unit has no inputs don't run it.
                  results[unit] = []
                  continue
  
              unjoined_outputs = list(
                  stats_accumulator(unit, unit_inputs, stats)
              )
              unit_outputs = self._join_lists_or_dicts(
                  unjoined_outputs, unit
              )
          results[unit] = unit_outputs
  
      self._set_stats(stats)
      return dict(
          (output.name, results[output]) for output in self.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 6 for calling the DAGPipeline obj's underlying member method code with example usage: obj.transform(input_object) and returns: dict