code: def testImmediateOutput(self):
      components = np.array(
          [0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 0, 0, 2, 2, 0, 0],
          dtype=np.int64,
      )
      dataset = dataset_ops.Dataset.from_tensor_slices(components)
      dataset = dataset.repeat(-1)
      dataset = dataset.group_by_window(
          key_func=lambda x: x % 3,
          reduce_func=lambda _, xs: xs.batch(4),
          window_size=4,
      )
      get_next = self.getNext(dataset)
      # The input is infinite, so this test demonstrates that:
      # 1. We produce output without having to consume the entire input,
      # 2. Different buckets can produce output at different rates, and
      # 3. For deterministic input, the output is deterministic.
      for _ in range(3):
          self.assertAllEqual([0, 0, 0, 0], self.evaluate(get_next()))
          self.assertAllEqual([1, 1, 1, 1], self.evaluate(get_next()))
          self.assertAllEqual([2, 2, 2, 2], self.evaluate(get_next()))
          self.assertAllEqual([0, 0, 0, 0], self.evaluate(get_next()))
  
imports: from absl.testing import parameterized
  import numpy
  from tensorflow.core.lib.core import error_codes_pb2
  from tensorflow.python.data.kernel_tests import checkpoint_test_base
  from tensorflow.python.data.kernel_tests import test_base
  from tensorflow.python.data.ops import dataset_ops
  from tensorflow.python.framework import combinations
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import errors
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import tensor_shape
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import string_ops
  from tensorflow.python.platform import test
label_desc: Use image node_id 7 for calling the GroupByWindowTest obj's underlying member method code with example usage: obj.testImmediateOutput() without return types