text: """Base class for directed graphs."""
  from copy import deepcopy
  
  import networkx as nx
  from networkx.classes.graph import Graph
  from networkx.classes.coreviews import AdjacencyView
  from networkx.classes.reportviews import (
      OutEdgeView,
      InEdgeView,
      DiDegreeView,
      InDegreeView,
      OutDegreeView,
  )
  from networkx.exception import NetworkXError
  import networkx.convert as convert
  
  __all__ = ["DiGraph"]
  
  
  class DiGraph(Graph):
      """
      Base class for directed graphs.
  
      A DiGraph stores nodes and edges with optional data, or attributes.
  
      DiGraphs hold directed edges.  Self loops are allowed but multiple
      (parallel) edges are not.
  
      Nodes can be arbitrary (hashable) Python objects with optional
      key/value attributes. By convention `None` is not used as a node.
  
      Edges are represented as links between nodes with optional
      key/value attributes.
  
      Parameters
      ----------
      incoming_graph_data : input graph (optional, default: None)
          Data to initialize graph. If None (default) an empty
          graph is created.  The data can be any format that is supported
          by the to_networkx_graph() function, currently including edge list,
          dict of dicts, dict of lists, NetworkX graph, NumPy matrix
          or 2d ndarray, SciPy sparse matrix, or PyGraphviz graph.
  
      attr : keyword arguments, optional (default= no attributes)
          Attributes to add to graph as key=value pairs.
  
      See Also
      --------
      Graph
      MultiGraph
      MultiDiGraph
      OrderedDiGraph
  
      Examples
      --------
      Create an empty graph structure (a "null graph") with no nodes and
      no edges.
  
      >>> G = nx.DiGraph()
  
      G can be grown in several ways.
  
      **Nodes:**
  
      Add one node at a time:
  
      >>> G.add_node(1)
  
      Add the nodes from any container (a list, dict, set or
      even the lines from a file or the nodes from another graph).
  
      >>> G.add_nodes_from([2, 3])
      >>> G.add_nodes_from(range(100, 110))
      >>> H = nx.path_graph(10)
      >>> G.add_nodes_from(H)
  
      In addition to strings and integers any hashable Python object
      (except None) can represent a node, e.g. a customized node object,
      or even another Graph.
  
      >>> G.add_node(H)
  
      **Edges:**
  
      G can also be grown by adding edges.
  
      Add one edge,
  
      >>> G.add_edge(1, 2)
  
      a list of edges,
  
      >>> G.add_edges_from([(1, 2), (1, 3)])
  
      or a collection of edges,
  
      >>> G.add_edges_from(H.edges)
  
      If some edges connect nodes not yet in the graph, the nodes
      are added automatically.  There are no errors when adding
      nodes or edges that already exist.
  
      **Attributes:**
  
      Each graph, node, and edge can hold key/value attribute pairs
      in an associated attribute dictionary (the keys must be hashable).
      By default these are empty, but can be added or changed using
      add_edge, add_node or direct manipulation of the attribute
      dictionaries named graph, node and edge respectively.
  
      >>> G = nx.DiGraph(day="Friday")
      >>> G.graph
      {'day': 'Friday'}
  
      Add node attributes using add_node(), add_nodes_from() or G.nodes
  
      >>> G.add_node(1, time="5pm")
      >>> G.add_nodes_from([3], time="2pm")
      >>> G.nodes[1]
      {'time': '5pm'}
      >>> G.nodes[1]["room"] = 714
      >>> del G.nodes[1]["room"]  # remove attribute
      >>> list(G.nodes(data=True))
      [(1, {'time': '5pm'}), (3, {'time': '2pm'})]
  
      Add edge attributes using add_edge(), add_edges_from(), subscript
      notation, or G.edges.
  
      >>> G.add_edge(1, 2, weight=4.7)
      >>> G.add_edges_from([(3, 4), (4, 5)], color="red")
      >>> G.add_edges_from([(1, 2, {"color": "blue"}), (2, 3, {"weight": 8})])
      >>> G[1][2]["weight"] = 4.7
      >>> G.edges[1, 2]["weight"] = 4
  
      Warning: we protect the graph data structure by making `G.edges[1, 2]` a
      read-only dict-like structure. However, you can assign to attributes
      in e.g. `G.edges[1, 2]`. Thus, use 2 sets of brackets to add/change
      data attributes: `G.edges[1, 2]['weight'] = 4`
      (For multigraphs: `MG.edges[u, v, key][name] = value`).
  
      **Shortcuts:**
  
      Many common graph features allow python syntax to speed reporting.
  
      >>> 1 in G  # check if node in graph
      True
      >>> [n for n in G if n < 3]  # iterate through nodes
      [1, 2]
      >>> len(G)  # number of nodes in graph
      5
  
      Often the best way to traverse all edges of a graph is via the neighbors.
      The neighbors are reported as an adjacency-dict `G.adj` or `G.adjacency()`
  
      >>> for n, nbrsdict in G.adjacency():
      ...     for nbr, eattr in nbrsdict.items():
      ...         if "weight" in eattr:
      ...             # Do something useful with the edges
      ...             pass
  
      But the edges reporting object is often more convenient:
  
      >>> for u, v, weight in G.edges(data="weight"):
      ...     if weight is not None:
      ...         # Do something useful with the edges
      ...         pass
  
      **Reporting:**
  
      Simple graph information is obtained using object-attributes and methods.
      Reporting usually provides views instead of containers to reduce memory
      usage. The views update as the graph is updated similarly to dict-views.
      The objects `nodes, `edges` and `adj` provide access to data attributes
      via lookup (e.g. `nodes[n], `edges[u, v]`, `adj[u][v]`) and iteration
      (e.g. `nodes.items()`, `nodes.data('color')`,
      `nodes.data('color', default='blue')` and similarly for `edges`)
      Views exist for `nodes`, `edges`, `neighbors()`/`adj` and `degree`.
  
      For details on these and other miscellaneous methods, see below.
  
      **Subclasses (Advanced):**
  
      The Graph class uses a dict-of-dict-of-dict data structure.
      The outer dict (node_dict) holds adjacency information keyed by node.
      The next dict (adjlist_dict) represents the adjacency information and holds
      edge data keyed by neighbor.  The inner dict (edge_attr_dict) represents
      the edge data and holds edge attribute values keyed by attribute names.
  
      Each of these three dicts can be replaced in a subclass by a user defined
      dict-like object. In general, the dict-like features should be
      maintained but extra features can be added. To replace one of the
      dicts create a new graph class by changing the class(!) variable
      holding the factory for that dict-like structure. The variable names are
      node_dict_factory, node_attr_dict_factory, adjlist_inner_dict_factory,
      adjlist_outer_dict_factory, edge_attr_dict_factory and graph_attr_dict_factory.
  
      node_dict_factory : function, (default: dict)
          Factory function to be used to create the dict containing node
          attributes, keyed by node id.
          It should require no arguments and return a dict-like object
  
      node_attr_dict_factory: function, (default: dict)
          Factory function to be used to create the node attribute
          dict which holds attribute values keyed by attribute name.
          It should require no arguments and return a dict-like object
  
      adjlist_outer_dict_factory : function, (default: dict)
          Factory function to be used to create the outer-most dict
          in the data structure that holds adjacency info keyed by node.
          It should require no arguments and return a dict-like object.
  
      adjlist_inner_dict_factory : function, optional (default: dict)
          Factory function to be used to create the adjacency list
          dict which holds edge data keyed by neighbor.
          It should require no arguments and return a dict-like object
  
      edge_attr_dict_factory : function, optional (default: dict)
          Factory function to be used to create the edge attribute
          dict which holds attribute values keyed by attribute name.
          It should require no arguments and return a dict-like object.
  
      graph_attr_dict_factory : function, (default: dict)
          Factory function to be used to create the graph attribute
          dict which holds attribute values keyed by attribute name.
          It should require no arguments and return a dict-like object.
  
      Typically, if your extension doesn't impact the data structure all
      methods will inherited without issue except: `to_directed/to_undirected`.
      By default these methods create a DiGraph/Graph class and you probably
      want them to create your extension of a DiGraph/Graph. To facilitate
      this we define two class variables that you can set in your subclass.
  
      to_directed_class : callable, (default: DiGraph or MultiDiGraph)
          Class to create a new graph structure in the `to_directed` method.
          If `None`, a NetworkX class (DiGraph or MultiDiGraph) is used.
  
      to_undirected_class : callable, (default: Graph or MultiGraph)
          Class to create a new graph structure in the `to_undirected` method.
          If `None`, a NetworkX class (Graph or MultiGraph) is used.
  
      **Subclassing Example**
  
      Create a low memory graph class that effectively disallows edge
      attributes by using a single attribute dict for all edges.
      This reduces the memory used, but you lose edge attributes.
  
      >>> class ThinGraph(nx.Graph):
      ...     all_edge_dict = {"weight": 1}
      ...
      ...     def single_edge_dict(self):
      ...         return self.all_edge_dict
      ...
      ...     edge_attr_dict_factory = single_edge_dict
      >>> G = ThinGraph()
      >>> G.add_edge(2, 1)
      >>> G[2][1]
      {'weight': 1}
      >>> G.add_edge(2, 2)
      >>> G[2][1] is G[2][2]
      True
  
  
      Please see :mod:`~networkx.classes.ordered` for more examples of
      creating graph subclasses by overwriting the base class `dict` with
      a dictionary-like object.
      """
  
      def __init__(self, incoming_graph_data=None, **attr):
          """Initialize a graph with edges, name, or graph attributes.
  
          Parameters
          ----------
          incoming_graph_data : input graph (optional, default: None)
              Data to initialize graph.  If None (default) an empty
              graph is created.  The data can be an edge list, or any
              NetworkX graph object.  If the corresponding optional Python
              packages are installed the data can also be a NumPy matrix
              or 2d ndarray, a SciPy sparse matrix, or a PyGraphviz graph.
  
          attr : keyword arguments, optional (default= no attributes)
              Attributes to add to graph as key=value pairs.
  
          See Also
          --------
          convert
  
          Examples
          --------
          >>> G = nx.Graph()  # or DiGraph, MultiGraph, MultiDiGraph, etc
          >>> G = nx.Graph(name="my graph")
          >>> e = [(1, 2), (2, 3), (3, 4)]  # list of edges
          >>> G = nx.Graph(e)
  
          Arbitrary graph attribute pairs (key=value) may be assigned
  
          >>> G = nx.Graph(e, day="Friday")
          >>> G.graph
          {'day': 'Friday'}
  
          """
          self.graph_attr_dict_factory = self.graph_attr_dict_factory
          self.node_dict_factory = self.node_dict_factory
          self.node_attr_dict_factory = self.node_attr_dict_factory
          self.adjlist_outer_dict_factory = self.adjlist_outer_dict_factory
          self.adjlist_inner_dict_factory = self.adjlist_inner_dict_factory
          self.edge_attr_dict_factory = self.edge_attr_dict_factory
  
          self.graph = self.graph_attr_dict_factory()  # dictionary for graph attributes
          self._node = self.node_dict_factory()  # dictionary for node attr
          # We store two adjacency lists:
          # the predecessors of node n are stored in the dict self._pred
          # the successors of node n are stored in the dict self._succ=self._adj
          self._adj = self.adjlist_outer_dict_factory()  # empty adjacency dict
          self._pred = self.adjlist_outer_dict_factory()  # predecessor
          self._succ = self._adj  # successor
  
          # attempt to load graph with data
          if incoming_graph_data is not None:
              convert.to_networkx_graph(incoming_graph_data, create_using=self)
          # load graph attributes (must be after convert)
          self.graph.update(attr)
  
      @property
      def adj(self):
          """Graph adjacency object holding the neighbors of each node.
  
          This object is a read-only dict-like structure with node keys
          and neighbor-dict values.  The neighbor-dict is keyed by neighbor
          to the edge-data-dict.  So `G.adj[3][2]['color'] = 'blue'` sets
          the color of the edge `(3, 2)` to `"blue"`.
  
          Iterating over G.adj behaves like a dict. Useful idioms include
          `for nbr, datadict in G.adj[n].items():`.
  
          The neighbor information is also provided by subscripting the graph.
          So `for nbr, foovalue in G[node].data('foo', default=1):` works.
  
          For directed graphs, `G.adj` holds outgoing (successor) info.
          """
          return AdjacencyView(self._succ)
  
      @property
      def succ(self):
          """Graph adjacency object holding the successors of each node.
  
          This object is a read-only dict-like structure with node keys
          and neighbor-dict values.  The neighbor-dict is keyed by neighbor
          to the edge-data-dict.  So `G.succ[3][2]['color'] = 'blue'` sets
          the color of the edge `(3, 2)` to `"blue"`.
  
          Iterating over G.succ behaves like a dict. Useful idioms include
          `for nbr, datadict in G.succ[n].items():`.  A data-view not provided
          by dicts also exists: `for nbr, foovalue in G.succ[node].data('foo'):`
          and a default can be set via a `default` argument to the `data` method.
  
          The neighbor information is also provided by subscripting the graph.
          So `for nbr, foovalue in G[node].data('foo', default=1):` works.
  
          For directed graphs, `G.adj` is identical to `G.succ`.
          """
          return AdjacencyView(self._succ)
  
      @property
      def pred(self):
          """Graph adjacency object holding the predecessors of each node.
  
          This object is a read-only dict-like structure with node keys
          and neighbor-dict values.  The neighbor-dict is keyed by neighbor
          to the edge-data-dict.  So `G.pred[2][3]['color'] = 'blue'` sets
          the color of the edge `(3, 2)` to `"blue"`.
  
          Iterating over G.pred behaves like a dict. Useful idioms include
          `for nbr, datadict in G.pred[n].items():`.  A data-view not provided
          by dicts also exists: `for nbr, foovalue in G.pred[node].data('foo'):`
          A default can be set via a `default` argument to the `data` method.
          """
          return AdjacencyView(self._pred)
  
      def add_node(self, node_for_adding, **attr):
          """Add a single node `node_for_adding` and update node attributes.
  
          Parameters
          ----------
          node_for_adding : node
              A node can be any hashable Python object except None.
          attr : keyword arguments, optional
              Set or change node attributes using key=value.
  
          See Also
          --------
          add_nodes_from
  
          Examples
          --------
          >>> G = nx.Graph()  # or DiGraph, MultiGraph, MultiDiGraph, etc
          >>> G.add_node(1)
          >>> G.add_node("Hello")
          >>> K3 = nx.Graph([(0, 1), (1, 2), (2, 0)])
          >>> G.add_node(K3)
          >>> G.number_of_nodes()
          3
  
          Use keywords set/change node attributes:
  
          >>> G.add_node(1, size=10)
          >>> G.add_node(3, weight=0.4, UTM=("13S", 382871, 3972649))
  
          Notes
          -----
          A hashable object is one that can be used as a key in a Python
          dictionary. This includes strings, numbers, tuples of strings
          and numbers, etc.
  
          On many platforms hashable items also include mutables such as
          NetworkX Graphs, though one should be careful that the hash
          doesn't change on mutables.
          """
          if node_for_adding not in self._succ:
              if node_for_adding is None:
                  raise ValueError("None cannot be a node")
              self._succ[node_for_adding] = self.adjlist_inner_dict_factory()
              self._pred[node_for_adding] = self.adjlist_inner_dict_factory()
              attr_dict = self._node[node_for_adding] = self.node_attr_dict_factory()
              attr_dict.update(attr)
          else:  # update attr even if node already exists
              self._node[node_for_adding].update(attr)
  
      def add_nodes_from(self, nodes_for_adding, **attr):
          """Add multiple nodes.
  
          Parameters
          ----------
          nodes_for_adding : iterable container
              A container of nodes (list, dict, set, etc.).
              OR
              A container of (node, attribute dict) tuples.
              Node attributes are updated using the attribute dict.
          attr : keyword arguments, optional (default= no attributes)
              Update attributes for all nodes in nodes.
              Node attributes specified in nodes as a tuple take
              precedence over attributes specified via keyword arguments.
  
          See Also
          --------
          add_node
  
          Examples
          --------
          >>> G = nx.Graph()  # or DiGraph, MultiGraph, MultiDiGraph, etc
          >>> G.add_nodes_from("Hello")
          >>> K3 = nx.Graph([(0, 1), (1, 2), (2, 0)])
          >>> G.add_nodes_from(K3)
          >>> sorted(G.nodes(), key=str)
          [0, 1, 2, 'H', 'e', 'l', 'o']
  
          Use keywords to update specific node attributes for every node.
  
          >>> G.add_nodes_from([1, 2], size=10)
          >>> G.add_nodes_from([3, 4], weight=0.4)
  
          Use (node, attrdict) tuples to update attributes for specific nodes.
  
          >>> G.add_nodes_from([(1, dict(size=11)), (2, {"color": "blue"})])
          >>> G.nodes[1]["size"]
          11
          >>> H = nx.Graph()
          >>> H.add_nodes_from(G.nodes(data=True))
          >>> H.nodes[1]["size"]
          11
  
          """
          for n in nodes_for_adding:
              try:
                  newnode = n not in self._node
                  newdict = attr
              except TypeError:
                  n, ndict = n
                  newnode = n not in self._node
                  newdict = attr.copy()
                  newdict.update(ndict)
              if newnode:
                  if n is None:
                      raise ValueError("None cannot be a node")
                  self._succ[n] = self.adjlist_inner_dict_factory()
                  self._pred[n] = self.adjlist_inner_dict_factory()
                  self._node[n] = self.node_attr_dict_factory()
              self._node[n].update(newdict)
  
      def remove_node(self, n):
          """Remove node n.
  
          Removes the node n and all adjacent edges.
          Attempting to remove a non-existent node will raise an exception.
  
          Parameters
          ----------
          n : node
             A node in the graph
  
          Raises
          ------
          NetworkXError
             If n is not in the graph.
  
          See Also
          --------
          remove_nodes_from
  
          Examples
          --------
          >>> G = nx.path_graph(3)  # or DiGraph, MultiGraph, MultiDiGraph, etc
          >>> list(G.edges)
          [(0, 1), (1, 2)]
          >>> G.remove_node(1)
          >>> list(G.edges)
          []
  
          """
          try:
              nbrs = self._succ[n]
              del self._node[n]
          except KeyError as err:  # NetworkXError if n not in self
              raise NetworkXError(f"The node {n} is not in the digraph.") from err
          for u in nbrs:
              del self._pred[u][n]  # remove all edges n-u in digraph
          del self._succ[n]  # remove node from succ
          for u in self._pred[n]:
              del self._succ[u][n]  # remove all edges n-u in digraph
          del self._pred[n]  # remove node from pred
  
      def remove_nodes_from(self, nodes):
          """Remove multiple nodes.
  
          Parameters
          ----------
          nodes : iterable container
              A container of nodes (list, dict, set, etc.).  If a node
              in the container is not in the graph it is silently ignored.
  
          See Also
          --------
          remove_node
  
          Examples
          --------
          >>> G = nx.path_graph(3)  # or DiGraph, MultiGraph, MultiDiGraph, etc
          >>> e = list(G.nodes)
          >>> e
          [0, 1, 2]
          >>> G.remove_nodes_from(e)
          >>> list(G.nodes)
          []
  
          """
          for n in nodes:
              try:
                  succs = self._succ[n]
                  del self._node[n]
                  for u in succs:
                      del self._pred[u][n]  # remove all edges n-u in digraph
                  del self._succ[n]  # now remove node
                  for u in self._pred[n]:
                      del self._succ[u][n]  # remove all edges n-u in digraph
                  del self._pred[n]  # now remove node
              except KeyError:
                  pass  # silent failure on remove
  
      def add_edge(self, u_of_edge, v_of_edge, **attr):
          """Add an edge between u and v.
  
          The nodes u and v will be automatically added if they are
          not already in the graph.
  
          Edge attributes can be specified with keywords or by directly
          accessing the edge's attribute dictionary. See examples below.
  
          Parameters
          ----------
          u_of_edge, v_of_edge : nodes
              Nodes can be, for example, strings or numbers.
              Nodes must be hashable (and not None) Python objects.
          attr : keyword arguments, optional
              Edge data (or labels or objects) can be assigned using
              keyword arguments.
  
          See Also
          --------
          add_edges_from : add a collection of edges
  
          Notes
          -----
          Adding an edge that already exists updates the edge data.
  
          Many NetworkX algorithms designed for weighted graphs use
          an edge attribute (by default `weight`) to hold a numerical value.
  
          Examples
          --------
          The following all add the edge e=(1, 2) to graph G:
  
          >>> G = nx.Graph()  # or DiGraph, MultiGraph, MultiDiGraph, etc
          >>> e = (1, 2)
          >>> G.add_edge(1, 2)  # explicit two-node form
          >>> G.add_edge(*e)  # single edge as tuple of two nodes
          >>> G.add_edges_from([(1, 2)])  # add edges from iterable container
  
          Associate data to edges using keywords:
  
          >>> G.add_edge(1, 2, weight=3)
          >>> G.add_edge(1, 3, weight=7, capacity=15, length=342.7)
  
          For non-string attribute keys, use subscript notation.
  
          >>> G.add_edge(1, 2)
          >>> G[1][2].update({0: 5})
          >>> G.edges[1, 2].update({0: 5})
          """
          u, v = u_of_edge, v_of_edge
          # add nodes
          if u not in self._succ:
              if u is None:
                  raise ValueError("None cannot be a node")
              self._succ[u] = self.adjlist_inner_dict_factory()
              self._pred[u] = self.adjlist_inner_dict_factory()
              self._node[u] = self.node_attr_dict_factory()
          if v not in self._succ:
              if v is None:
                  raise ValueError("None cannot be a node")
              self._succ[v] = self.adjlist_inner_dict_factory()
              self._pred[v] = self.adjlist_inner_dict_factory()
              self._node[v] = self.node_attr_dict_factory()
          # add the edge
          datadict = self._adj[u].get(v, self.edge_attr_dict_factory())
          datadict.update(attr)
          self._succ[u][v] = datadict
          self._pred[v][u] = datadict
  
      def add_edges_from(self, ebunch_to_add, **attr):
          """Add all the edges in ebunch_to_add.
  
          Parameters
          ----------
          ebunch_to_add : container of edges
              Each edge given in the container will be added to the
              graph. The edges must be given as 2-tuples (u, v) or
              3-tuples (u, v, d) where d is a dictionary containing edge data.
          attr : keyword arguments, optional
              Edge data (or labels or objects) can be assigned using
              keyword arguments.
  
          See Also
          --------
          add_edge : add a single edge
          add_weighted_edges_from : convenient way to add weighted edges
  
          Notes
          -----
          Adding the same edge twice has no effect but any edge data
          will be updated when each duplicate edge is added.
  
          Edge attributes specified in an ebunch take precedence over
          attributes specified via keyword arguments.
  
          Examples
          --------
          >>> G = nx.Graph()  # or DiGraph, MultiGraph, MultiDiGraph, etc
          >>> G.add_edges_from([(0, 1), (1, 2)])  # using a list of edge tuples
          >>> e = zip(range(0, 3), range(1, 4))
          >>> G.add_edges_from(e)  # Add the path graph 0-1-2-3
  
          Associate data to edges
  
          >>> G.add_edges_from([(1, 2), (2, 3)], weight=3)
          >>> G.add_edges_from([(3, 4), (1, 4)], label="WN2898")
          """
          for e in ebunch_to_add:
              ne = len(e)
              if ne == 3:
                  u, v, dd = e
              elif ne == 2:
                  u, v = e
                  dd = {}
              else:
                  raise NetworkXError(f"Edge tuple {e} must be a 2-tuple or 3-tuple.")
              if u not in self._succ:
                  if u is None:
                      raise ValueError("None cannot be a node")
                  self._succ[u] = self.adjlist_inner_dict_factory()
                  self._pred[u] = self.adjlist_inner_dict_factory()
                  self._node[u] = self.node_attr_dict_factory()
              if v not in self._succ:
                  if v is None:
                      raise ValueError("None cannot be a node")
                  self._succ[v] = self.adjlist_inner_dict_factory()
                  self._pred[v] = self.adjlist_inner_dict_factory()
                  self._node[v] = self.node_attr_dict_factory()
              datadict = self._adj[u].get(v, self.edge_attr_dict_factory())
              datadict.update(attr)
              datadict.update(dd)
              self._succ[u][v] = datadict
              self._pred[v][u] = datadict
  
      def remove_edge(self, u, v):
          """Remove the edge between u and v.
  
          Parameters
          ----------
          u, v : nodes
              Remove the edge between nodes u and v.
  
          Raises
          ------
          NetworkXError
              If there is not an edge between u and v.
  
          See Also
          --------
          remove_edges_from : remove a collection of edges
  
          Examples
          --------
          >>> G = nx.Graph()  # or DiGraph, etc
          >>> nx.add_path(G, [0, 1, 2, 3])
          >>> G.remove_edge(0, 1)
          >>> e = (1, 2)
          >>> G.remove_edge(*e)  # unpacks e from an edge tuple
          >>> e = (2, 3, {"weight": 7})  # an edge with attribute data
          >>> G.remove_edge(*e[:2])  # select first part of edge tuple
          """
          try:
              del self._succ[u][v]
              del self._pred[v][u]
          except KeyError as err:
              raise NetworkXError(f"The edge {u}-{v} not in graph.") from err
  
      def remove_edges_from(self, ebunch):
          """Remove all edges specified in ebunch.
  
          Parameters
          ----------
          ebunch: list or container of edge tuples
              Each edge given in the list or container will be removed
              from the graph. The edges can be:
  
                  - 2-tuples (u, v) edge between u and v.
                  - 3-tuples (u, v, k) where k is ignored.
  
          See Also
          --------
          remove_edge : remove a single edge
  
          Notes
          -----
          Will fail silently if an edge in ebunch is not in the graph.
  
          Examples
          --------
          >>> G = nx.path_graph(4)  # or DiGraph, MultiGraph, MultiDiGraph, etc
          >>> ebunch = [(1, 2), (2, 3)]
          >>> G.remove_edges_from(ebunch)
          """
          for e in ebunch:
              u, v = e[:2]  # ignore edge data
              if u in self._succ and v in self._succ[u]:
                  del self._succ[u][v]
                  del self._pred[v][u]
  
      def has_successor(self, u, v):
          """Returns True if node u has successor v.
  
          This is true if graph has the edge u->v.
          """
          return u in self._succ and v in self._succ[u]
  
      def has_predecessor(self, u, v):
          """Returns True if node u has predecessor v.
  
          This is true if graph has the edge u<-v.
          """
          return u in self._pred and v in self._pred[u]
  
      def successors(self, n):
          """Returns an iterator over successor nodes of n.
  
          A successor of n is a node m such that there exists a directed
          edge from n to m.
  
          Parameters
          ----------
          n : node
             A node in the graph
  
          Raises
          ------
          NetworkXError
             If n is not in the graph.
  
          See Also
          --------
          predecessors
  
          Notes
          -----
          neighbors() and successors() are the same.
          """
          try:
              return iter(self._succ[n])
          except KeyError as err:
              raise NetworkXError(f"The node {n} is not in the digraph.") from err
  
      # digraph definitions
      neighbors = successors
  
      def predecessors(self, n):
          """Returns an iterator over predecessor nodes of n.
  
          A predecessor of n is a node m such that there exists a directed
          edge from m to n.
  
          Parameters
          ----------
          n : node
             A node in the graph
  
          Raises
          ------
          NetworkXError
             If n is not in the graph.
  
          See Also
          --------
          successors
          """
          try:
              return iter(self._pred[n])
          except KeyError as err:
              raise NetworkXError(f"The node {n} is not in the digraph.") from err
  
      @property
      def edges(self):
          """An OutEdgeView of the DiGraph as G.edges or G.edges().
  
          edges(self, nbunch=None, data=False, default=None)
  
          The OutEdgeView provides set-like operations on the edge-tuples
          as well as edge attribute lookup. When called, it also provides
          an EdgeDataView object which allows control of access to edge
          attributes (but does not provide set-like operations).
          Hence, `G.edges[u, v]['color']` provides the value of the color
          attribute for edge `(u, v)` while
          `for (u, v, c) in G.edges.data('color', default='red'):`
          iterates through all the edges yielding the color attribute
          with default `'red'` if no color attribute exists.
  
          Parameters
          ----------
          nbunch : single node, container, or all nodes (default= all nodes)
              The view will only report edges incident to these nodes.
          data : string or bool, optional (default=False)
              The edge attribute returned in 3-tuple (u, v, ddict[data]).
              If True, return edge attribute dict in 3-tuple (u, v, ddict).
              If False, return 2-tuple (u, v).
          default : value, optional (default=None)
              Value used for edges that don't have the requested attribute.
              Only relevant if data is not True or False.
  
          Returns
          -------
          edges : OutEdgeView
              A view of edge attributes, usually it iterates over (u, v)
              or (u, v, d) tuples of edges, but can also be used for
              attribute lookup as `edges[u, v]['foo']`.
  
          See Also
          --------
          in_edges, out_edges
  
          Notes
          -----
          Nodes in nbunch that are not in the graph will be (quietly) ignored.
          For directed graphs this returns the out-edges.
  
          Examples
          --------
          >>> G = nx.DiGraph()  # or MultiDiGraph, etc
          >>> nx.add_path(G, [0, 1, 2])
          >>> G.add_edge(2, 3, weight=5)
          >>> [e for e in G.edges]
          [(0, 1), (1, 2), (2, 3)]
          >>> G.edges.data()  # default data is {} (empty dict)
          OutEdgeDataView([(0, 1, {}), (1, 2, {}), (2, 3, {'weight': 5})])
          >>> G.edges.data("weight", default=1)
          OutEdgeDataView([(0, 1, 1), (1, 2, 1), (2, 3, 5)])
          >>> G.edges([0, 2])  # only edges incident to these nodes
          OutEdgeDataView([(0, 1), (2, 3)])
          >>> G.edges(0)  # only edges incident to a single node (use G.adj[0]?)
          OutEdgeDataView([(0, 1)])
  
          """
          return OutEdgeView(self)
  
      # alias out_edges to edges
      out_edges = edges
  
      @property
      def in_edges(self):
          """An InEdgeView of the Graph as G.in_edges or G.in_edges().
  
          in_edges(self, nbunch=None, data=False, default=None):
  
          Parameters
          ----------
          nbunch : single node, container, or all nodes (default= all nodes)
              The view will only report edges incident to these nodes.
          data : string or bool, optional (default=False)
              The edge attribute returned in 3-tuple (u, v, ddict[data]).
              If True, return edge attribute dict in 3-tuple (u, v, ddict).
              If False, return 2-tuple (u, v).
          default : value, optional (default=None)
              Value used for edges that don't have the requested attribute.
              Only relevant if data is not True or False.
  
          Returns
          -------
          in_edges : InEdgeView
              A view of edge attributes, usually it iterates over (u, v)
              or (u, v, d) tuples of edges, but can also be used for
              attribute lookup as `edges[u, v]['foo']`.
  
          See Also
          --------
          edges
          """
          return InEdgeView(self)
  
      @property
      def degree(self):
          """A DegreeView for the Graph as G.degree or G.degree().
  
          The node degree is the number of edges adjacent to the node.
          The weighted node degree is the sum of the edge weights for
          edges incident to that node.
  
          This object provides an iterator for (node, degree) as well as
          lookup for the degree for a single node.
  
          Parameters
          ----------
          nbunch : single node, container, or all nodes (default= all nodes)
              The view will only report edges incident to these nodes.
  
          weight : string or None, optional (default=None)
             The name of an edge attribute that holds the numerical value used
             as a weight.  If None, then each edge has weight 1.
             The degree is the sum of the edge weights adjacent to the node.
  
          Returns
          -------
          If a single node is requested
          deg : int
              Degree of the node
  
          OR if multiple nodes are requested
          nd_iter : iterator
              The iterator returns two-tuples of (node, degree).
  
          See Also
          --------
          in_degree, out_degree
  
          Examples
          --------
          >>> G = nx.DiGraph()  # or MultiDiGraph
          >>> nx.add_path(G, [0, 1, 2, 3])
          >>> G.degree(0)  # node 0 with degree 1
          1
          >>> list(G.degree([0, 1, 2]))
          [(0, 1), (1, 2), (2, 2)]
  
          """
          return DiDegreeView(self)
  
      @property
      def in_degree(self):
          """An InDegreeView for (node, in_degree) or in_degree for single node.
  
          The node in_degree is the number of edges pointing to the node.
          The weighted node degree is the sum of the edge weights for
          edges incident to that node.
  
          This object provides an iteration over (node, in_degree) as well as
          lookup for the degree for a single node.
  
          Parameters
          ----------
          nbunch : single node, container, or all nodes (default= all nodes)
              The view will only report edges incident to these nodes.
  
          weight : string or None, optional (default=None)
             The name of an edge attribute that holds the numerical value used
             as a weight.  If None, then each edge has weight 1.
             The degree is the sum of the edge weights adjacent to the node.
  
          Returns
          -------
          If a single node is requested
          deg : int
              In-degree of the node
  
          OR if multiple nodes are requested
          nd_iter : iterator
              The iterator returns two-tuples of (node, in-degree).
  
          See Also
          --------
          degree, out_degree
  
          Examples
          --------
          >>> G = nx.DiGraph()
          >>> nx.add_path(G, [0, 1, 2, 3])
          >>> G.in_degree(0)  # node 0 with degree 0
          0
          >>> list(G.in_degree([0, 1, 2]))
          [(0, 0), (1, 1), (2, 1)]
  
          """
          return InDegreeView(self)
  
      @property
      def out_degree(self):
          """An OutDegreeView for (node, out_degree)
  
          The node out_degree is the number of edges pointing out of the node.
          The weighted node degree is the sum of the edge weights for
          edges incident to that node.
  
          This object provides an iterator over (node, out_degree) as well as
          lookup for the degree for a single node.
  
          Parameters
          ----------
          nbunch : single node, container, or all nodes (default= all nodes)
              The view will only report edges incident to these nodes.
  
          weight : string or None, optional (default=None)
             The name of an edge attribute that holds the numerical value used
             as a weight.  If None, then each edge has weight 1.
             The degree is the sum of the edge weights adjacent to the node.
  
          Returns
          -------
          If a single node is requested
          deg : int
              Out-degree of the node
  
          OR if multiple nodes are requested
          nd_iter : iterator
              The iterator returns two-tuples of (node, out-degree).
  
          See Also
          --------
          degree, in_degree
  
          Examples
          --------
          >>> G = nx.DiGraph()
          >>> nx.add_path(G, [0, 1, 2, 3])
          >>> G.out_degree(0)  # node 0 with degree 1
          1
          >>> list(G.out_degree([0, 1, 2]))
          [(0, 1), (1, 1), (2, 1)]
  
          """
          return OutDegreeView(self)
  
      def clear(self):
          """Remove all nodes and edges from the graph.
  
          This also removes the name, and all graph, node, and edge attributes.
  
          Examples
          --------
          >>> G = nx.path_graph(4)  # or DiGraph, MultiGraph, MultiDiGraph, etc
          >>> G.clear()
          >>> list(G.nodes)
          []
          >>> list(G.edges)
          []
  
          """
          self._succ.clear()
          self._pred.clear()
          self._node.clear()
          self.graph.clear()
  
      def clear_edges(self):
          """Remove all edges from the graph without altering nodes.
  
          Examples
          --------
          >>> G = nx.path_graph(4)  # or DiGraph, MultiGraph, MultiDiGraph, etc
          >>> G.clear_edges()
          >>> list(G.nodes)
          [0, 1, 2, 3]
          >>> list(G.edges)
          []
  
          """
          for predecessor_dict in self._pred.values():
              predecessor_dict.clear()
          for successor_dict in self._succ.values():
              successor_dict.clear()
  
      def is_multigraph(self):
          """Returns True if graph is a multigraph, False otherwise."""
          return False
  
      def is_directed(self):
          """Returns True if graph is directed, False otherwise."""
          return True
  
      def to_undirected(self, reciprocal=False, as_view=False):
          """Returns an undirected representation of the digraph.
  
          Parameters
          ----------
          reciprocal : bool (optional)
            If True only keep edges that appear in both directions
            in the original digraph.
          as_view : bool (optional, default=False)
            If True return an undirected view of the original directed graph.
  
          Returns
          -------
          G : Graph
              An undirected graph with the same name and nodes and
              with edge (u, v, data) if either (u, v, data) or (v, u, data)
              is in the digraph.  If both edges exist in digraph and
              their edge data is different, only one edge is created
              with an arbitrary choice of which edge data to use.
              You must check and correct for this manually if desired.
  
          See Also
          --------
          Graph, copy, add_edge, add_edges_from
  
          Notes
          -----
          If edges in both directions (u, v) and (v, u) exist in the
          graph, attributes for the new undirected edge will be a combination of
          the attributes of the directed edges.  The edge data is updated
          in the (arbitrary) order that the edges are encountered.  For
          more customized control of the edge attributes use add_edge().
  
          This returns a "deepcopy" of the edge, node, and
          graph attributes which attempts to completely copy
          all of the data and references.
  
          This is in contrast to the similar G=DiGraph(D) which returns a
          shallow copy of the data.
  
          See the Python copy module for more information on shallow
          and deep copies, https://docs.python.org/3/library/copy.html.
  
          Warning: If you have subclassed DiGraph to use dict-like objects
          in the data structure, those changes do not transfer to the
          Graph created by this method.
  
          Examples
          --------
          >>> G = nx.path_graph(2)  # or MultiGraph, etc
          >>> H = G.to_directed()
          >>> list(H.edges)
          [(0, 1), (1, 0)]
          >>> G2 = H.to_undirected()
          >>> list(G2.edges)
          [(0, 1)]
          """
          graph_class = self.to_undirected_class()
          if as_view is True:
              return nx.graphviews.generic_graph_view(self, graph_class)
          # deepcopy when not a view
          G = graph_class()
          G.graph.update(deepcopy(self.graph))
          G.add_nodes_from((n, deepcopy(d)) for n, d in self._node.items())
          if reciprocal is True:
              G.add_edges_from(
                  (u, v, deepcopy(d))
                  for u, nbrs in self._adj.items()
                  for v, d in nbrs.items()
                  if v in self._pred[u]
              )
          else:
              G.add_edges_from(
                  (u, v, deepcopy(d))
                  for u, nbrs in self._adj.items()
                  for v, d in nbrs.items()
              )
          return G
  
      def reverse(self, copy=True):
          """Returns the reverse of the graph.
  
          The reverse is a graph with the same nodes and edges
          but with the directions of the edges reversed.
  
          Parameters
          ----------
          copy : bool optional (default=True)
              If True, return a new DiGraph holding the reversed edges.
              If False, the reverse graph is created using a view of
              the original graph.
          """
          if copy:
              H = self.__class__()
              H.graph.update(deepcopy(self.graph))
              H.add_nodes_from((n, deepcopy(d)) for n, d in self.nodes.items())
              H.add_edges_from((v, u, deepcopy(d)) for u, v, d in self.edges(data=True))
              return H
          return nx.graphviews.reverse_view(self)
  