code: def get(self, n):
      len_ = len(self.array)
      # not enough element in 'array'
      if len_ < n:
          logging.debug(
              f"there are really few items in class {self.class_}"
          )
          self.reset()
          np.random.shuffle(self.array)
          mul = n // len_
          rest = n - mul * len_
          return np.concatenate(
              (np.tile(self.array, mul), self.array[:rest])
          )
  
      # not enough element in array's tail
      if len_ - self.i < n:
          self.reset()
  
      if self.i == 0:
          np.random.shuffle(self.array)
      i = self.i
      self.i += n
      return self.array[i : self.i]
  
imports: import logging
  import numpy
  import torch.utils.data
  import torchvision
  from torch.utils.data.sampler import BatchSampler, RandomSampler
label_desc: Use image node_id 3 for calling the InfiniteSliceIterator obj's underlying member method code with example usage: obj.get(n) and returns: self, np