Update docstring for load_from_networkx

src/pymatching/matching.py

@@ -212,7 +212,7 @@ def add_edge(
                                      error_probability, has_error_probability)
 
     def load_from_networkx(self, graph: nx.Graph) -> None:
-        """
+        r"""
         Load a matching graph from a NetworkX graph
 
         Parameters
@@ -222,13 +222,10 @@ def load_from_networkx(self, graph: nx.Graph) -> None:
             `m` in `G` should be an integer :math:`0<m<M-1`, and each node should
             be unique. Each edge in the NetworkX graph can have optional
             attributes ``qubit_id``, ``weight`` and ``error_probability``.
-            ``qubit_id`` should be an int or a set of ints. If there
-            are :math:`N` qubits then the union of all ints in the ``qubit_id``
-            attributes in the graph should be the integers :math:`0\ldots N-1`.
+            ``qubit_id`` should be an int or a set of ints.
             Note that the ``qubit_id`` attribute can instead be used to store the indices
             of logical observables flipped by an error on the corresponding edge
             (e.g. a frame change in an error instruction in a stim detector error model).
-            If there are N logical observables, they should again be numbered :math:`0\ldots N-1`.
             Each ``weight`` attribute should be a non-negative float. If
             every edge is assigned an error_probability between zero and one,
             then the ``add_noise`` method can be used to simulate noise and
@@ -242,8 +239,8 @@ def load_from_networkx(self, graph: nx.Graph) -> None:
         >>> g = nx.Graph()
         >>> g.add_edge(0, 1, qubit_id=0, weight=math.log((1-0.1)/0.1), error_probability=0.1)
         >>> g.add_edge(1, 2, qubit_id=1, weight=math.log((1-0.15)/0.15), error_probability=0.15)
-        >>> g.node[0]['is_boundary'] = True
-        >>> g.node[2]['is_boundary'] = True
+        >>> g.nodes[0]['is_boundary'] = True
+        >>> g.nodes[2]['is_boundary'] = True
         >>> m = pymatching.Matching(g)
         >>> m
         <pymatching.Matching object with 2 qubits, 1 detector, 2 boundary nodes, and 2 edges>