code: def minibatch(*tensors, **kwargs):
      batch_size = kwargs.get("batch_size", 128)
  
      if len(tensors) == 1:
          tensor = tensors[0]
          for i in range(0, len(tensor), batch_size):
              yield tensor[i : i + batch_size]
      else:
          for i in range(0, len(tensors[0]), batch_size):
              yield tuple(x[i : i + batch_size] for x in tensors)
  
imports: import paddle
  from paddle import nn, optimizer
  import numpy
  from paddle import log
  from dataloader import BasicDataset
  from paddle.io import Dataset
  from time import time
  from sklearn.metrics import roc_auc_score
  import random
  import os
  from tqdm import tqdm
label_desc: Use image node_id 3 for calling a global function with example usage: minibatch() without return types