[Doc] Labor sampling improvement (#5221)

* [Doc] Labor sampling improvement

When importance sampling option is used, the returned blocks include edge weights that need to be used during the message passing operation. So, I am including this in the documentation.

* Add documentation to weighted sampling case also

python/dgl/dataloading/labor_sampler.py

@@ -53,12 +53,16 @@ class LaborSampler(BlockSampler):
     prob : str, optional
         If given, the probability of each neighbor being sampled is proportional
         to the edge feature value with the given name in ``g.edata``.
-        The feature must be a scalar on each edge.
+        The feature must be a scalar on each edge. In this case, the returned
+        blocks edata include ``'edge_weights'`` that needs to be used in the
+        message passing operation.
     importance_sampling : int, default ``0``
         Whether to use importance sampling or uniform sampling, use of negative
         values optimizes importance sampling probabilities until convergence
         while use of positive values runs optimization steps that many times.
-        If the value is i, then LABOR-i variant is used.
+        If the value is i, then LABOR-i variant is used. When used with a
+        nonzero parameter, the returned blocks edata include ``'edge_weights'``
+        that needs to be used in the message passing operation.
     layer_dependency : bool, default ``False``
         Specifies whether different layers should use same random variates.
         Results into a reduction in the number of vertices sampled, but may