code: def testFromMultipleConcurrentGenerators(self):
      num_inner_repeats = 5
      num_outer_repeats = 20
  
      def generator():
          for i in range(1, 10):
              yield ([i] * i, [i, i**2, i**3])
  
      input_list = list(generator())
  
      # The interleave transformation is essentially a flat map that draws from
      # multiple input datasets concurrently (in a cyclic fashion). By placing
      # `Dataset.from_generator()` inside an interleave, we test its behavior when
      # multiple iterators are active at the same time; by additionally
      # prefetching inside the interleave, we create the possibility of concurrent
      # invocations to several iterators created by the same dataset.
      def interleave_fn(_):
          return (
              dataset_ops.Dataset.from_generator(
                  generator,
                  output_types=(dtypes.int64, dtypes.int64),
                  output_shapes=([None], [3]),
              )
              .repeat(num_inner_repeats)
              .prefetch(5)
          )
  
      dataset = dataset_ops.Dataset.range(num_outer_repeats).interleave(
          interleave_fn, cycle_length=10, block_length=len(input_list)
      )
      get_next = self.getNext(dataset)
      for _ in range(num_inner_repeats * num_outer_repeats):
          for elem in input_list:
              val0, val1 = self.evaluate(get_next())
              self.assertAllEqual(elem[0], val0)
              self.assertAllEqual(elem[1], val1)
      with self.assertRaises(errors.OutOfRangeError):
          self.evaluate(get_next())
  
imports: import threading
  from absl.testing import parameterized
  import numpy
  from tensorflow.python.data.kernel_tests import test_base
  from tensorflow.python.data.ops import dataset_ops
  from tensorflow.python.data.ops import from_generator_op
  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 sparse_tensor
  from tensorflow.python.framework import tensor_spec
  from tensorflow.python.ops import script_ops
  from tensorflow.python.ops import sparse_ops
  from tensorflow.python.ops.ragged import ragged_factory_ops
  from tensorflow.python.ops.ragged import ragged_tensor
  from tensorflow.python.platform import test
label_desc: Use image node_id 6 for calling the FromGeneratorTest obj's underlying member method code with example usage: obj.testFromMultipleConcurrentGenerators() and returns: dataset_ops