fix doc. (#1100)

python/dgl/contrib/sampling/sampler.py

@@ -493,21 +493,23 @@ class EdgeSampler(object):
     When negative edges are created, a batch of negative edges are also placed
     in a subgraph.
 
-    Currently, negative_mode only supports:
-        'head': the negative edges are generated by corrupting head nodes
-            with uniformly randomly sampled nodes,
-        'tail': the negative edges are generated by corrupting tail nodes
-            with uniformly randomly sampled nodes,
-        'PBG-head': the negative edges are generated by corrupting a set
-            of head nodes with the same set of nodes uniformly randomly sampled
+    Currently, `negative_mode` only supports:
+
+    * 'head': the negative edges are generated by corrupting head nodes with uniformly randomly sampled nodes,
+
+    * 'tail': the negative edges are generated by corrupting tail nodes with uniformly randomly sampled nodes,
+
+    * 'PBG-head': the negative edges are generated by corrupting a set \
+    of head nodes with the same set of nodes uniformly randomly sampled \
     from the graph. Please see Pytorch-BigGraph for more details.
-        'PBG-tail': the negative edges are generated by corrupting a set
+
+    * 'PBG-tail': the negative edges are generated by corrupting a set \
     of tail nodes with the same set of nodes similar to 'PBG-head'.
 
     The sampler returns EdgeSubgraph, where a user can access the unique head nodes
     and tail nodes directly.
 
-    When the flag return_false_neg is turned on, the sampler will also check
+    When the flag `return_false_neg` is turned on, the sampler will also check
     if the generated negative edges are true negative edges and will return
     a vector that indicates false negative edges. The vector is stored in
     the negative graph as `false_neg` edge data.