code: def regenerate_intermediates_for_dataset(
      dataset, force_tf_compat_v1=True, max_num_examples=None
  ):
      """Regenerate intermediate outputs required for the benchmark."""
  
      common_variables = _get_common_variables(
          dataset, force_tf_compat_v1
      )
  
      logging.info(
          "Regenerating intermediate outputs required for benchmark."
      )
      with beam.Pipeline() as p:
          _ = p | _AnalyzeAndTransformDataset(
              dataset,
              common_variables.tfxio,
              common_variables.preprocessing_fn,
              common_variables.transform_input_dataset_metadata,
              force_tf_compat_v1=force_tf_compat_v1,
              max_num_examples=max_num_examples,
              generate_dataset=True,
          )
      logging.info("Intermediate outputs regenerated.")
  
imports: import collections
  import shutil
  import tempfile
  import time
  from absl import logging
  import apache_beam
  from apache_beam.utils import shared
  import tensorflow
  import tensorflow_transform
  from tensorflow_transform import graph_tools
  from tensorflow_transform import impl_helper
  import tensorflow_transform.beam
  from tensorflow_transform.beam import impl
  from tensorflow_transform.saved import saved_transform_io
  from tensorflow_transform.saved import saved_transform_io_v2
  from tensorflow_transform.tf_metadata import dataset_metadata
  from tensorflow_transform.tf_metadata import schema_utils
  import tfx
  from tfx.benchmarks import benchmark_utils
  from tfx.benchmarks import benchmark_base
  from tfx_bsl.coders import example_coder
  from tfx_bsl.tfxio import tensor_adapter
  from tfx_bsl.tfxio import tf_example_record
label_desc: Use image node_id 2 for calling a global function with example usage: regenerate_intermediates_for_dataset(dataset, force_tf_compat_v1, max_num_examples) without return types