[Doc]modify the wrong code in initialize the hetero_graph negative sampler (#2382) (#2411)

* modify the wrong code in initialize the hetero_graph negative sampler

the etype in hetero graph should be name of edges
self.weights = {
            etype: g.in_degrees(etype=etype).float() ** 0.75
            for _, etype, _ in g.canonical_etypes
        }
the original code will give etype a Tuple format, it cannot apply to the next processing:
self.weights[etype].multinomial(len(src), replacement=True)
and I add the part which confusing me to generate eid_dict

* fix
Co-authored-by: Quan Gan <coin2028@hotmail.com>
Co-authored-by: Shaow <coco11563@yeah.net>

docs/source/guide/minibatch-link.rst

@@ -240,7 +240,8 @@ source-destination array pairs. An example is given as follows:
            # caches the probability distribution
            self.weights = {
                etype: g.in_degrees(etype=etype).float() ** 0.75
-                for etype in g.canonical_etypes}
+               for _, etype, _ in g.canonical_etypes
+           }
            self.k = k
 
        def __call__(self, g, eids_dict):
@@ -248,10 +249,18 @@ source-destination array pairs. An example is given as follows:
            for etype, eids in eids_dict.items():
                src, _ = g.find_edges(eids, etype=etype)
                src = src.repeat_interleave(self.k)
-                dst = self.weights.multinomial(len(src), replacement=True)
+               dst = self.weights[etype].multinomial(len(src), replacement=True)
                result_dict[etype] = (src, dst)
            return result_dict
 
+Then you can give the dataloader a dictionary of edge types and edge IDs as well as the negative
+sampler.  For instance, the following iterates over all edges of the heterogeneous graph.
+
+.. code:: python
+    train_eid_dict = {
+        g.edges(etype=etype, form='eid')
+        for etype in g.etypes}
+
     dataloader = dgl.dataloading.EdgeDataLoader(
         g, train_eid_dict, sampler,
         negative_sampler=NegativeSampler(g, 5),