code: def __next__(self):
      random_seed(12345 * self.i + 4321)
  
      def generate(dim):
          shape = random_int(1, 10, [dim]).tolist()
          if self.as_tensor:
              return random_array([self.batch_size] + shape)
          else:
              return [
                  random_array(shape) for _ in range(self.batch_size)
              ]
  
      if self.i < self.n:
          self.i += 1
          if isinstance(self.dims, (list, tuple)):
              data = [generate(d) for d in self.dims]
          else:
              data = generate(self.dims)
  
          # it needs to keep data alive
          self.data.append(data)
  
          def add_one(x):
              if isinstance(x, list):
                  for elm in x:
                      elm = add_one(elm)
              else:
                  x += 1
              return x
  
          if len(self.data) > self.num_keep_samples:
              tmp = self.data.pop(0)
              # change popped data to make sure it is corrupted
              tmp = add_one(tmp)
          return data
      else:
          self.i = 0
          raise StopIteration
  
imports: import functools
  import numpy
  import nvidia.dali.fn
  import nvidia.dali.ops
  from nvidia.dali import Pipeline
  from nose_utils import assert_raises
  from test_utils import check_output
label_desc: Use image node_id 4 for calling the TestIteratorZeroCopy obj's underlying member method code with example usage: obj.__next__() and returns: data, random_array, unknown, x