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+r"""This module provides functions and operations for bipartite
+graphs.  Bipartite graphs `B = (U, V, E)` have two node sets `U,V` and edges in
+`E` that only connect nodes from opposite sets. It is common in the literature
+to use an spatial analogy referring to the two node sets as top and bottom nodes.
+
+The bipartite algorithms are not imported into the networkx namespace
+at the top level so the easiest way to use them is with:
+
+>>> from networkx.algorithms import bipartite
+
+NetworkX does not have a custom bipartite graph class but the Graph()
+or DiGraph() classes can be used to represent bipartite graphs. However,
+you have to keep track of which set each node belongs to, and make
+sure that there is no edge between nodes of the same set. The convention used
+in NetworkX is to use a node attribute named `bipartite` with values 0 or 1 to
+identify the sets each node belongs to. This convention is not enforced in
+the source code of bipartite functions, it's only a recommendation.
+
+For example:
+
+>>> B = nx.Graph()
+>>> # Add nodes with the node attribute "bipartite"
+>>> B.add_nodes_from([1, 2, 3, 4], bipartite=0)
+>>> B.add_nodes_from(["a", "b", "c"], bipartite=1)
+>>> # Add edges only between nodes of opposite node sets
+>>> B.add_edges_from([(1, "a"), (1, "b"), (2, "b"), (2, "c"), (3, "c"), (4, "a")])
+
+Many algorithms of the bipartite module of NetworkX require, as an argument, a
+container with all the nodes that belong to one set, in addition to the bipartite
+graph `B`. The functions in the bipartite package do not check that the node set
+is actually correct nor that the input graph is actually bipartite.
+If `B` is connected, you can find the two node sets using a two-coloring
+algorithm:
+
+>>> nx.is_connected(B)
+True
+>>> bottom_nodes, top_nodes = bipartite.sets(B)
+
+However, if the input graph is not connected, there are more than one possible
+colorations. This is the reason why we require the user to pass a container
+with all nodes of one bipartite node set as an argument to most bipartite
+functions. In the face of ambiguity, we refuse the temptation to guess and
+raise an :exc:`AmbiguousSolution <networkx.AmbiguousSolution>`
+Exception if the input graph for
+:func:`bipartite.sets <networkx.algorithms.bipartite.basic.sets>`
+is disconnected.
+
+Using the `bipartite` node attribute, you can easily get the two node sets:
+
+>>> top_nodes = {n for n, d in B.nodes(data=True) if d["bipartite"] == 0}
+>>> bottom_nodes = set(B) - top_nodes
+
+So you can easily use the bipartite algorithms that require, as an argument, a
+container with all nodes that belong to one node set:
+
+>>> print(round(bipartite.density(B, bottom_nodes), 2))
+0.5
+>>> G = bipartite.projected_graph(B, top_nodes)
+
+All bipartite graph generators in NetworkX build bipartite graphs with the
+`bipartite` node attribute. Thus, you can use the same approach:
+
+>>> RB = bipartite.random_graph(5, 7, 0.2)
+>>> RB_top = {n for n, d in RB.nodes(data=True) if d["bipartite"] == 0}
+>>> RB_bottom = set(RB) - RB_top
+>>> list(RB_top)
+[0, 1, 2, 3, 4]
+>>> list(RB_bottom)
+[5, 6, 7, 8, 9, 10, 11]
+
+For other bipartite graph generators see
+:mod:`Generators <networkx.algorithms.bipartite.generators>`.
+
+"""
+
+from networkx.algorithms.bipartite.basic import *
+from networkx.algorithms.bipartite.centrality import *
+from networkx.algorithms.bipartite.cluster import *
+from networkx.algorithms.bipartite.covering import *
+from networkx.algorithms.bipartite.edgelist import *
+from networkx.algorithms.bipartite.matching import *
+from networkx.algorithms.bipartite.matrix import *
+from networkx.algorithms.bipartite.projection import *
+from networkx.algorithms.bipartite.redundancy import *
+from networkx.algorithms.bipartite.spectral import *
+from networkx.algorithms.bipartite.generators import *
+from networkx.algorithms.bipartite.extendability import *