We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
metric_closure will throw KeyError with unconnected graph
Suggest checking connectedness with `nx.is_connected()` on entry to `metric_closure()` and throwing a more informative error if not.

--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `networkx/algorithms/approximation/steinertree.py`
Content:
```
1 from itertools import combinations, chain
2 
3 from networkx.utils import pairwise, not_implemented_for
4 import networkx as nx
5 
6 __all__ = ['metric_closure', 'steiner_tree']
7 
8 
9 @not_implemented_for('directed')
10 def metric_closure(G, weight='weight'):
11     """  Return the metric closure of a graph.
12 
13     The metric closure of a graph *G* is the complete graph in which each edge
14     is weighted by the shortest path distance between the nodes in *G* .
15 
16     Parameters
17     ----------
18     G : NetworkX graph
19 
20     Returns
21     -------
22     NetworkX graph
23         Metric closure of the graph `G`.
24 
25     """
26     M = nx.Graph()
27 
28     seen = set()
29     Gnodes = set(G)
30     for u, (distance, path) in nx.all_pairs_dijkstra(G, weight=weight):
31         seen.add(u)
32         for v in Gnodes - seen:
33             M.add_edge(u, v, distance=distance[v], path=path[v])
34 
35     return M
36 
37 
38 @not_implemented_for('directed')
39 def steiner_tree(G, terminal_nodes, weight='weight'):
40     """ Return an approximation to the minimum Steiner tree of a graph.
41 
42     Parameters
43     ----------
44     G : NetworkX graph
45 
46     terminal_nodes : list
47          A list of terminal nodes for which minimum steiner tree is
48          to be found.
49 
50     Returns
51     -------
52     NetworkX graph
53         Approximation to the minimum steiner tree of `G` induced by
54         `terminal_nodes` .
55 
56     Notes
57     -----
58     Steiner tree can be approximated by computing the minimum spanning
59     tree of the subgraph of the metric closure of the graph induced by the
60     terminal nodes, where the metric closure of *G* is the complete graph in
61     which each edge is weighted by the shortest path distance between the
62     nodes in *G* .
63     This algorithm produces a tree whose weight is within a (2 - (2 / t))
64     factor of the weight of the optimal Steiner tree where *t* is number of
65     terminal nodes.
66 
67     """
68     # M is the subgraph of the metric closure induced by the terminal nodes of
69     # G.
70     M = metric_closure(G, weight=weight)
71     # Use the 'distance' attribute of each edge provided by the metric closure
72     # graph.
73     H = M.subgraph(terminal_nodes)
74     mst_edges = nx.minimum_spanning_edges(H, weight='distance', data=True)
75     # Create an iterator over each edge in each shortest path; repeats are okay
76     edges = chain.from_iterable(pairwise(d['path']) for u, v, d in mst_edges)
77     T = G.edge_subgraph(edges)
78     return T
79
```
--- END FILES ---

Please first localize the bug based on the issue statement, and then generate a patch according to the `git diff` format fenced by three backticks.

Here is an example:

```diff
diff --git a/examples/server_async.py b/examples/server_async.py
--- a/examples/server_async.py
+++ b/examples/server_async.py
@@ -313,4 +313,4 @@
 
 
 if __name__ == "__main__":
-    asyncio.run(run_async_server("."), debug=True)
+    asyncio.run(run_async_server(), debug=True)
diff --git a/examples/server_sync.py b/examples/server_sync.py
--- a/examples/server_sync.py
+++ b/examples/server_sync.py
@@ -313,5 +313,5 @@
 
 
 if __name__ == "__main__":
-    server = run_sync_server(".")
+    server = run_sync_server()
     server.shutdown()

```

diff --git a/networkx/algorithms/approximation/steinertree.py b/networkx/algorithms/approximation/steinertree.py
--- a/networkx/algorithms/approximation/steinertree.py
+++ b/networkx/algorithms/approximation/steinertree.py
@@ -25,11 +25,22 @@
     """
     M = nx.Graph()
 
-    seen = set()
     Gnodes = set(G)
-    for u, (distance, path) in nx.all_pairs_dijkstra(G, weight=weight):
-        seen.add(u)
-        for v in Gnodes - seen:
+
+    # check for connected graph while processing first node
+    all_paths_iter = nx.all_pairs_dijkstra(G, weight=weight)
+    u, (distance, path) = next(all_paths_iter)
+    if Gnodes - set(distance):
+        msg = "G is not a connected graph. metric_closure is not defined."
+        raise nx.NetworkXError(msg)
+    Gnodes.remove(u)
+    for v in Gnodes:
+        M.add_edge(u, v, distance=distance[v], path=path[v])
+
+    # first node done -- now process the rest
+    for u, (distance, path) in all_paths_iter:
+        Gnodes.remove(u)
+        for v in Gnodes:
             M.add_edge(u, v, distance=distance[v], path=path[v])
 
     return M

{"golden_diff": "diff --git a/networkx/algorithms/approximation/steinertree.py b/networkx/algorithms/approximation/steinertree.py\n--- a/networkx/algorithms/approximation/steinertree.py\n+++ b/networkx/algorithms/approximation/steinertree.py\n@@ -25,11 +25,22 @@\n     \"\"\"\n     M = nx.Graph()\n \n-    seen = set()\n     Gnodes = set(G)\n-    for u, (distance, path) in nx.all_pairs_dijkstra(G, weight=weight):\n-        seen.add(u)\n-        for v in Gnodes - seen:\n+\n+    # check for connected graph while processing first node\n+    all_paths_iter = nx.all_pairs_dijkstra(G, weight=weight)\n+    u, (distance, path) = next(all_paths_iter)\n+    if Gnodes - set(distance):\n+        msg = \"G is not a connected graph. metric_closure is not defined.\"\n+        raise nx.NetworkXError(msg)\n+    Gnodes.remove(u)\n+    for v in Gnodes:\n+        M.add_edge(u, v, distance=distance[v], path=path[v])\n+\n+    # first node done -- now process the rest\n+    for u, (distance, path) in all_paths_iter:\n+        Gnodes.remove(u)\n+        for v in Gnodes:\n             M.add_edge(u, v, distance=distance[v], path=path[v])\n \n     return M\n", "issue": "metric_closure will throw KeyError with unconnected graph\nSuggest checking connectedness with `nx.is_connected()` on entry to `metric_closure()` and throwing a more informative error if not.\n", "before_files": [{"content": "from itertools import combinations, chain\n\nfrom networkx.utils import pairwise, not_implemented_for\nimport networkx as nx\n\n__all__ = ['metric_closure', 'steiner_tree']\n\n\n@not_implemented_for('directed')\ndef metric_closure(G, weight='weight'):\n    \"\"\"  Return the metric closure of a graph.\n\n    The metric closure of a graph *G* is the complete graph in which each edge\n    is weighted by the shortest path distance between the nodes in *G* .\n\n    Parameters\n    ----------\n    G : NetworkX graph\n\n    Returns\n    -------\n    NetworkX graph\n        Metric closure of the graph `G`.\n\n    \"\"\"\n    M = nx.Graph()\n\n    seen = set()\n    Gnodes = set(G)\n    for u, (distance, path) in nx.all_pairs_dijkstra(G, weight=weight):\n        seen.add(u)\n        for v in Gnodes - seen:\n            M.add_edge(u, v, distance=distance[v], path=path[v])\n\n    return M\n\n\n@not_implemented_for('directed')\ndef steiner_tree(G, terminal_nodes, weight='weight'):\n    \"\"\" Return an approximation to the minimum Steiner tree of a graph.\n\n    Parameters\n    ----------\n    G : NetworkX graph\n\n    terminal_nodes : list\n         A list of terminal nodes for which minimum steiner tree is\n         to be found.\n\n    Returns\n    -------\n    NetworkX graph\n        Approximation to the minimum steiner tree of `G` induced by\n        `terminal_nodes` .\n\n    Notes\n    -----\n    Steiner tree can be approximated by computing the minimum spanning\n    tree of the subgraph of the metric closure of the graph induced by the\n    terminal nodes, where the metric closure of *G* is the complete graph in\n    which each edge is weighted by the shortest path distance between the\n    nodes in *G* .\n    This algorithm produces a tree whose weight is within a (2 - (2 / t))\n    factor of the weight of the optimal Steiner tree where *t* is number of\n    terminal nodes.\n\n    \"\"\"\n    # M is the subgraph of the metric closure induced by the terminal nodes of\n    # G.\n    M = metric_closure(G, weight=weight)\n    # Use the 'distance' attribute of each edge provided by the metric closure\n    # graph.\n    H = M.subgraph(terminal_nodes)\n    mst_edges = nx.minimum_spanning_edges(H, weight='distance', data=True)\n    # Create an iterator over each edge in each shortest path; repeats are okay\n    edges = chain.from_iterable(pairwise(d['path']) for u, v, d in mst_edges)\n    T = G.edge_subgraph(edges)\n    return T\n", "path": "networkx/algorithms/approximation/steinertree.py"}], "after_files": [{"content": "from itertools import combinations, chain\n\nfrom networkx.utils import pairwise, not_implemented_for\nimport networkx as nx\n\n__all__ = ['metric_closure', 'steiner_tree']\n\n\n@not_implemented_for('directed')\ndef metric_closure(G, weight='weight'):\n    \"\"\"  Return the metric closure of a graph.\n\n    The metric closure of a graph *G* is the complete graph in which each edge\n    is weighted by the shortest path distance between the nodes in *G* .\n\n    Parameters\n    ----------\n    G : NetworkX graph\n\n    Returns\n    -------\n    NetworkX graph\n        Metric closure of the graph `G`.\n\n    \"\"\"\n    M = nx.Graph()\n\n    Gnodes = set(G)\n\n    # check for connected graph while processing first node\n    all_paths_iter = nx.all_pairs_dijkstra(G, weight=weight)\n    u, (distance, path) = next(all_paths_iter)\n    if Gnodes - set(distance):\n        msg = \"G is not a connected graph. metric_closure is not defined.\"\n        raise nx.NetworkXError(msg)\n    Gnodes.remove(u)\n    for v in Gnodes:\n        M.add_edge(u, v, distance=distance[v], path=path[v])\n\n    # first node done -- now process the rest\n    for u, (distance, path) in all_paths_iter:\n        Gnodes.remove(u)\n        for v in Gnodes:\n            M.add_edge(u, v, distance=distance[v], path=path[v])\n\n    return M\n\n\n@not_implemented_for('directed')\ndef steiner_tree(G, terminal_nodes, weight='weight'):\n    \"\"\" Return an approximation to the minimum Steiner tree of a graph.\n\n    Parameters\n    ----------\n    G : NetworkX graph\n\n    terminal_nodes : list\n         A list of terminal nodes for which minimum steiner tree is\n         to be found.\n\n    Returns\n    -------\n    NetworkX graph\n        Approximation to the minimum steiner tree of `G` induced by\n        `terminal_nodes` .\n\n    Notes\n    -----\n    Steiner tree can be approximated by computing the minimum spanning\n    tree of the subgraph of the metric closure of the graph induced by the\n    terminal nodes, where the metric closure of *G* is the complete graph in\n    which each edge is weighted by the shortest path distance between the\n    nodes in *G* .\n    This algorithm produces a tree whose weight is within a (2 - (2 / t))\n    factor of the weight of the optimal Steiner tree where *t* is number of\n    terminal nodes.\n\n    \"\"\"\n    # M is the subgraph of the metric closure induced by the terminal nodes of\n    # G.\n    M = metric_closure(G, weight=weight)\n    # Use the 'distance' attribute of each edge provided by the metric closure\n    # graph.\n    H = M.subgraph(terminal_nodes)\n    mst_edges = nx.minimum_spanning_edges(H, weight='distance', data=True)\n    # Create an iterator over each edge in each shortest path; repeats are okay\n    edges = chain.from_iterable(pairwise(d['path']) for u, v, d in mst_edges)\n    T = G.edge_subgraph(edges)\n    return T\n", "path": "networkx/algorithms/approximation/steinertree.py"}]}