code: def testNestedFlatMapDataset(self):
      repeats = [[1, 2], [3, 4], [5, 0], [1, 7]]
      components = np.array(repeats, dtype=np.int64)
      dataset = dataset_ops.Dataset.from_tensor_slices(
          components
      ).flat_map(
          lambda x: dataset_ops.Dataset.from_tensor_slices(x).flat_map(
              lambda y: dataset_ops.Dataset.from_tensors(y).repeat(y)
          )
      )
      expected_output = []
      for row in repeats:
          for i in row:
              expected_output.extend([i] * i)
      self.assertDatasetProduces(
          dataset, expected_output=expected_output
      )
  
imports: import random
  from absl.testing import parameterized
  import numpy
  from tensorflow.python.client import session
  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.data.ops import options
  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 function
  from tensorflow.python.framework import sparse_tensor
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import random_ops
  from tensorflow.python.ops import sparse_ops
  from tensorflow.python.ops import stateless_random_ops
  from tensorflow.python.ops import tensor_array_ops
  from tensorflow.python.ops import variable_scope
  from tensorflow.python.ops.ragged import ragged_conversion_ops
  from tensorflow.python.ops.ragged import ragged_tensor
  from tensorflow.python.platform import test
  from tensorflow.python.training import server_lib
label_desc: Use image node_id 2 for calling the FlatMapTest obj's underlying member method code with example usage: obj.testNestedFlatMapDataset() without return types