class_docstr: An abstract representation of Pipeline Term dependencies.
  
  This class does not keep any additional metadata about any term relations
  other than dependency ordering.  As such it is only useful in contexts
  where you care exclusively about order properties (for example, when
  drawing visualizations of execution order).
  
  Parameters
  ----------
  terms : dict
      A dict mapping names to final output terms.
  
  Attributes
  ----------
  outputs
  
  Methods
  -------
  ordered()
      Return a topologically-sorted iterator over the terms in self.
  
  See Also
  --------
  ExecutionPlan
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code: def __init__(self, terms):
      self.graph = DiGraph()
  
      self._frozen = False
      parents = set()
      for term in itervalues(terms):
          self._add_to_graph(term, parents)
          # No parents should be left between top-level terms.
          assert not parents
  
      self._outputs = terms
  
      # Mark that no more terms should be added to the graph.
      self._frozen = True
  
imports: from networkx import DiGraph, topological_sort
  from six import iteritems, itervalues
  from catalyst.utils.memoize import lazyval
  from catalyst.pipeline.visualize import display_graph
  from .term import LoadableTerm
label_desc: Use image node_id 1 to create a new TermGraph object from inherited base classes: object with example: obj = TermGraph(terms)