parameters: (self, depth_level=1, states=False)
return_statement: return super().iterTreePhysical(depth_level)
docstring: A view of the hierarchical tree, iterating
          over the modules as well as the leaf-nodes.
  
          Parameters
          ----------
          depth_level : int, optional
              The module level returned by ``iterator.module_id``. Set to ``1``
              (default) to return the top modules (coarsest level). Set to ``2``
              for second coarsest level etc. Set to ``-1`` to return the bottom
              level modules (finest level).
          states : bool, optional
              For higher-order networks, if ``states`` is True, it will iterate
              over state nodes. Otherwise it will iterate over physical nodes,
              merging state nodes with same ``node_id`` if they are in the same
              module. Note that the same physical node may end up on different
              paths in the tree.
              Default ``false``.
  
          Notes
          ----
          For higher-order networks, each node is represented by a set of state
          nodes with the same ``node_id``, where each state node represents a
          different constraint on the random walker. This enables
          overlapping modules, where state nodes with the same ``node_id`` end up
          in different modules. However, the state nodes with the same
          ``node_id`` within each module are only visible as one (partial)
          physical node (if ``states = False``).
  
          Returns
          -------
          InfomapIterator or InfomapIteratorPhysical
              An iterator over each node in the tree, depth first from the root
docstring_summary: A view of the hierarchical tree, iterating
          over the modules as well as the leaf-nodes.
docstring_tokens: A view of the hierarchical tree iterating over the modules as well as the leaf - nodes .
function: def get_tree(self, depth_level=1, states=False):
          """A view of the hierarchical tree, iterating
          over the modules as well as the leaf-nodes.
  
          Parameters
          ----------
          depth_level : int, optional
              The module level returned by ``iterator.module_id``. Set to ``1``
              (default) to return the top modules (coarsest level). Set to ``2``
              for second coarsest level etc. Set to ``-1`` to return the bottom
              level modules (finest level).
          states : bool, optional
              For higher-order networks, if ``states`` is True, it will iterate
              over state nodes. Otherwise it will iterate over physical nodes,
              merging state nodes with same ``node_id`` if they are in the same
              module. Note that the same physical node may end up on different
              paths in the tree.
              Default ``false``.
  
          Notes
          ----
          For higher-order networks, each node is represented by a set of state
          nodes with the same ``node_id``, where each state node represents a
          different constraint on the random walker. This enables
          overlapping modules, where state nodes with the same ``node_id`` end up
          in different modules. However, the state nodes with the same
          ``node_id`` within each module are only visible as one (partial)
          physical node (if ``states = False``).
  
          Returns
          -------
          InfomapIterator or InfomapIteratorPhysical
              An iterator over each node in the tree, depth first from the root
          """
          if states:
              return super().iterTree(depth_level)
          return super().iterTreePhysical(depth_level)
function_tokens: def get_tree ( self , depth_level = 1 , states = False ) : if states : return super ( ) . iterTree ( depth_level ) return super ( ) . iterTreePhysical ( depth_level )