class_docstr: Dictionary wrapper of **ItemSet**.
  
  Each item is retrieved by iterating over each itemset and returned with
  corresponding key as a dict.
  
  Parameters
  ----------
  itemsets: Dict[str, ItemSet]
  
  Examples
  --------
  >>> import torch
  >>> from dgl import graphbolt as gb
  
  1. Single iterable: seed nodes.
  
  >>> node_ids_user = torch.arange(0, 5)
  >>> node_ids_item = torch.arange(5, 10)
  >>> item_set = gb.ItemSetDict({
  ...     "user": gb.ItemSet(node_ids_user, names="seed_nodes"),
  ...     "item": gb.ItemSet(node_ids_item, names="seed_nodes")})
  >>> list(item_set)
  [{"user": tensor(0)}, {"user": tensor(1)}, {"user": tensor(2)},
   {"user": tensor(3)}, {"user": tensor(4)}, {"item": tensor(5)},
   {"item": tensor(6)}, {"item": tensor(7)}, {"item": tensor(8)},
   {"item": tensor(9)}}]
  >>> item_set[:]
  {"user": tensor([0, 1, 2, 3, 4]), "item": tensor([5, 6, 7, 8, 9])}
  >>> item_set.names
  ('seed_nodes',)
  
  2. Tuple of iterables with same shape: seed nodes and labels.
  
  >>> node_ids_user = torch.arange(0, 2)
  >>> labels_user = torch.arange(0, 2)
  >>> node_ids_item = torch.arange(2, 5)
  >>> labels_item = torch.arange(2, 5)
  >>> item_set = gb.ItemSetDict({
  ...     "user": gb.ItemSet(
  ...         (node_ids_user, labels_user),
  ...         names=("seed_nodes", "labels")),
  ...     "item": gb.ItemSet(
  ...         (node_ids_item, labels_item),
  ...         names=("seed_nodes", "labels"))})
  >>> list(item_set)
  [{"user": (tensor(0), tensor(0))}, {"user": (tensor(1), tensor(1))},
   {"item": (tensor(2), tensor(2))}, {"item": (tensor(3), tensor(3))},
   {"item": (tensor(4), tensor(4))}}]
  >>> item_set[:]
  {"user": (tensor([0, 1]), tensor([0, 1])),
   "item": (tensor([2, 3, 4]), tensor([2, 3, 4]))}
  >>> item_set.names
  ('seed_nodes', 'labels')
  
  3. Tuple of iterables with different shape: node pairs and negative dsts.
  
  >>> node_pairs_like = torch.arange(0, 4).reshape(-1, 2)
  >>> neg_dsts_like = torch.arange(4, 10).reshape(-1, 3)
  >>> node_pairs_follow = torch.arange(0, 6).reshape(-1, 2)
  >>> neg_dsts_follow = torch.arange(6, 15).reshape(-1, 3)
  >>> item_set = gb.ItemSetDict({
  ...     "user:like:item": gb.ItemSet(
  ...         (node_pairs_like, neg_dsts_like),
  ...         names=("node_pairs", "negative_dsts")),
  ...     "user:follow:user": gb.ItemSet(
  ...         (node_pairs_follow, neg_dsts_follow),
  ...         names=("node_pairs", "negative_dsts"))})
  >>> list(item_set)
  [{"user:like:item": (tensor([0, 1]), tensor([4, 5, 6]))},
   {"user:like:item": (tensor([2, 3]), tensor([7, 8, 9]))},
   {"user:follow:user": (tensor([0, 1]), tensor([ 6,  7,  8,  9, 10, 11]))},
   {"user:follow:user": (tensor([2, 3]), tensor([12, 13, 14, 15, 16, 17]))},
   {"user:follow:user": (tensor([4, 5]), tensor([18, 19, 20, 21, 22, 23]))}]
  >>> item_set[:]
  {"user:like:item": (tensor([[0, 1], [2, 3]]),
                      tensor([[4, 5, 6], [7, 8, 9]])),
   "user:follow:user": (tensor([[0, 1], [2, 3], [4, 5]]),
                        tensor([[ 6,  7,  8,  9, 10, 11],
                                [12, 13, 14, 15, 16, 17],
                                [18, 19, 20, 21, 22, 23]]))}
  >>> item_set.names
  ('node_pairs', 'negative_dsts')
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code: def __repr__(self) -> str:
      ret = (
          "{Classname}(\n"
          "    itemsets={itemsets},\n"
          "    names={names},\n"
          ")"
      )
  
      itemsets_str = textwrap.indent(
          repr(self._itemsets), " " * len("    itemsets=")
      ).strip()
  
      return ret.format(
          Classname=self.__class__.__name__,
          itemsets=itemsets_str,
          names=self._names,
      )
  
file_path: dgl/python/dgl/graphbolt/itemset.py
imports: import textwrap
  from typing import Dict, Iterable, Iterator, Sized, Tuple, Union
  import torch
label_desc: Use image node_id 6 for calling the ItemSetDict obj's underlying member method code with example usage: obj.__repr__() and returns: ret