code: def testMapDict(self):
      dataset = (
          dataset_ops.Dataset.range(10)
          .map(lambda x: {"foo": x * 2, "bar": x**2})
          .flat_map(
              lambda d: dataset_ops.Dataset.from_tensors(
                  d["foo"]
              ).repeat(d["bar"])
          )
      )
      get_next = self.getNext(dataset)
      for i in range(10):
          for _ in range(i**2):
              self.assertEqual(i * 2, self.evaluate(get_next()))
      with self.assertRaises(errors.OutOfRangeError):
          self.evaluate(get_next())
  
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 4 for calling the FlatMapTest obj's underlying member method code with example usage: obj.testMapDict() without return types