code: def test_tf_data_builder_multiple_versions(self):
      ds_name = "my_dataset"
      data_dir = self.tmp_dir
      builder_a = adhoc_builder.store_as_tfds_dataset(
          name=ds_name,
          version="1.0.0",
          split_datasets={
              "train": tf.data.Dataset.from_tensor_slices({"a": [1]})
          },
          features=tfds.features.FeaturesDict({"a": tf.int64}),
          data_dir=data_dir,
      )
  
      builder_b = adhoc_builder.store_as_tfds_dataset(
          name=ds_name,
          version="2.0.0",
          split_datasets={
              "train": tf.data.Dataset.from_tensor_slices({"a": [2]})
          },
          features=tfds.features.FeaturesDict({"a": tf.int64}),
          data_dir=data_dir,
      )
  
      assert builder_a.data_dir == os.path.join(
          self.tmp_dir, ds_name, "1.0.0"
      )
      assert builder_b.data_dir == os.path.join(
          self.tmp_dir, ds_name, "2.0.0"
      )
  
      train1 = tfds.load(
          f"{ds_name}:1.0.0", split="train", data_dir=data_dir
      )
      assert list(dataset_utils.as_numpy(train1)) == [{"a": 1}]
  
      train2 = tfds.load(
          f"{ds_name}:2.0.0", split="train", data_dir=data_dir
      )
      assert list(dataset_utils.as_numpy(train2)) == [{"a": 2}]
  
imports: import os
  from typing import Iterable
  import apache_beam
  from apache_beam.testing.test_pipeline import TestPipeline
  import numpy
  import tensorflow
  from tensorflow_datasets import testing
  from tensorflow_datasets.core import dataset_builder
  from tensorflow_datasets.core import dataset_utils
  from tensorflow_datasets.core.dataset_builders import adhoc_builder
  import tensorflow_datasets.public_api
label_desc: Use image node_id 3 for calling the TfDataBuilderTest obj's underlying member method code with example usage: obj.test_tf_data_builder_multiple_versions() without return types