code: def __init__(self, g, user_type, item_type, batch_size):
      self.g = g
      self.user_type = user_type
      self.item_type = item_type
      self.user_to_item_etype = list(
          g.metagraph()[user_type][item_type]
      )[0]
      self.item_to_user_etype = list(
          g.metagraph()[item_type][user_type]
      )[0]
      self.batch_size = batch_size
  
imports: import argparse
  import pickle
  import time
  import dgl
  import dgl.function
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.utils.data import DataLoader, IterableDataset
  from ..None import utils
label_desc: Use image node_id 1 to create a new ItemToItemBatchSampler object from inherited base classes: IterableDataset with example: obj = ItemToItemBatchSampler(g, user_type, item_type, batch_size)