[GraphBolt] add typing hint for ItemSet/ItemSetDic/MinibatchSampler (#5841)

python/dgl/graphbolt/itemset.py

 """GraphBolt Itemset."""
 
+from typing import Dict, Iterable, Iterator, Tuple
+
 __all__ = ["ItemSet", "ItemSetDict"]
 
 
@@ -45,7 +47,7 @@ class ItemSet:
      (tensor(4), tensor(9), tensor([18, 19]))]
     """
 
-    def __init__(self, items):
+    def __init__(self, items: Iterable or Tuple[Iterable]) -> None:
         if isinstance(items, tuple):
             assert all(
                 items[0].size(0) == item.size(0) for item in items
@@ -54,7 +56,7 @@ class ItemSet:
         else:
             self._items = (items,)
 
-    def __iter__(self):
+    def __iter__(self) -> Iterator:
         if len(self._items) == 1:
             yield from self._items[0]
             return
@@ -119,10 +121,10 @@ class ItemSetDict:
      {('user', 'follow', 'user'): (tensor(2), tensor(5), tensor([4, 5]))}]
     """
 
-    def __init__(self, itemsets):
+    def __init__(self, itemsets: Dict[str, ItemSet]) -> None:
         self._itemsets = itemsets
 
-    def __iter__(self):
+    def __iter__(self) -> Iterator:
         for key, itemset in self._itemsets.items():
             for item in itemset:
                 yield {key: item}

python/dgl/graphbolt/minibatch_sampler.py

@@ -2,7 +2,7 @@
 
 from collections.abc import Mapping
 from functools import partial
-from typing import Optional
+from typing import Iterator, Optional
 
 from torch.utils.data import default_collate
 from torchdata.datapipes.iter import IterableWrapper, IterDataPipe
@@ -174,14 +174,14 @@ class MinibatchSampler(IterDataPipe):
         batch_size: int,
         drop_last: Optional[bool] = False,
         shuffle: Optional[bool] = False,
-    ):
+    ) -> None:
         super().__init__()
         self._item_set = item_set
         self._batch_size = batch_size
         self._drop_last = drop_last
         self._shuffle = shuffle
 
-    def __iter__(self):
+    def __iter__(self) -> Iterator:
         data_pipe = IterableWrapper(self._item_set)
         # Shuffle before batch.
         if self._shuffle: