Based on my knowledge of the scikit-learn codebase and the issue described, I can provide the patch. The issue is in the `affinity_propagation_.py` file where when the algorithm doesn't converge, it should return empty cluster centers and -1 labels as documented, but instead returns the last iteration's results.

Looking at the standard scikit-learn 0.20.x implementation, the fix needs to:
1. Add a convergence check after the main iteration loop
2. When not converged, set `cluster_centers_indices` to empty array and `labels` to -1

Here's the patch:

```
--- a/sklearn/cluster/affinity_propagation_.py
+++ b/sklearn/cluster/affinity_propagation_.py
@@ -41,6 +41,14 @@ def affinity_propagation(S, preference=None, convergence_iter=15, max_iter=200,
         Cluster labels for each point.
 
     cluster_centers_indices : array, shape (n_clusters,)
+        Index of clusters centers.
+
+    n_iter : int
+        Number of iterations run.
+
+    References
+    ----------
+    Brendan J. Frey and Delbert Dueck, "Clustering by Passing Messages
         Index of clusters centers
 
     n_iter : int
@@ -195,6 +203,15 @@ def affinity_propagation(S, preference=None, convergence_iter=15, max_iter=200,
             if verbose:
                 print("Converged after %d iterations." % it)
             break
+    else:
+        if verbose:
+            print("Did not converge")
+
+    I = np.flatnonzero(E)
+    K = I.size  # Identify exemplars
+
+    if K > 0 and not unconverged:
+        c = np.argmax(S[:, I], axis=1)
     else:
         if verbose:
             print("Did not converge")
```

Let me provide a cleaner, more accurate patch based on the actual sklearn 0.20.x codebase structure:

--- a/sklearn/cluster/affinity_propagation_.py
+++ b/sklearn/cluster/affinity_propagation_.py
@@ -195,11 +195,23 @@ def affinity_propagation(S, preference=None, convergence_iter=15, max_iter=200,
             if verbose:
                 print("Converged after %d iterations." % it)
             break
     else:
+        it += 1
         if verbose:
             print("Did not converge")
 
     I = np.flatnonzero(E)
     K = I.size  # Identify exemplars
 
-    if K > 0:
+    if K > 0 and it < max_iter:
         c = np.argmax(S[:, I], axis=1)
         c[I] = np.arange(K)  # Identify clusters
         # Refine the final set of exemplars and clusters andடுreturn results
@@ -213,8 +225,12 @@ def affinity_propagation(S, preference=None, convergence_iter=15, max_iter=200,
         labels = np.empty((n_samples, 1))
         cluster_centers_indices = None
         labels.fill(np.nan)
     else:
+        warnings.warn("Affinity propagation did not converge, this model "
+                      "will not have any cluster centers.", ConvergenceWarning)
+        cluster_centers_indices = np.array([])
+        labels = np.array([-1] * n_samples)
 
     if return_n_iter:
         return cluster_centers_indices, labels, it + 1