code: def _split_generators(self, dl_manager, pipeline):
      del dl_manager
  
      examples = (
          pipeline | beam.Create(range(1000)) | beam.Map(_gen_example)
      )
  
      # Wrap the pipeline inside a ptransform_fn to add `'label' >> ` to avoid
      # duplicated PTransform nodes names.
      generate_examples = beam.ptransform_fn(self._generate_examples)
      return {
          "train": examples
          | "train" >> generate_examples(num_examples=1000),
          "test": examples
          | "test" >> generate_examples(num_examples=725),
      }
  
imports: import pathlib
  from typing import Callable
  from unittest import mock
  import apache_beam
  from etils import epath
  import numpy
  import pytest
  import tensorflow
  from tensorflow_datasets.core import dataset_builder
  from tensorflow_datasets.core import dataset_info
  from tensorflow_datasets.core import dataset_utils
  from tensorflow_datasets.core import download
  from tensorflow_datasets.core import features
  from tensorflow_datasets.core import utils
label_desc: Use image node_id 1 for calling the CommonPipelineDummyBeamDataset obj's underlying member method code with example usage: obj._split_generators(dl_manager, pipeline) and returns: dict