code: def save_subset_weights_to_hdf5_group(f, weights):
      """Save top-level weights of a model to a HDF5 group.
  
      Args:
          f: HDF5 group.
          weights: List of weight variables.
      """
      weight_values = [backend.convert_to_numpy(w) for w in weights]
      weight_names = [w.name.encode("utf8") for w in weights]
      save_attributes_to_hdf5_group(f, "weight_names", weight_names)
      for name, val in zip(weight_names, weight_values):
          param_dset = f.create_dataset(
              name, val.shape, dtype=val.dtype
          )
          if not val.shape:
              # scalar
              param_dset[()] = val
          else:
              param_dset[:] = val
  
imports: import json
  import os
  import warnings
  import numpy
  from absl import logging
  from keras import backend
  from keras import optimizers
  from keras.backend.common import global_state
  from keras.legacy.saving import json_utils
  from keras.legacy.saving import saving_options
  from keras.legacy.saving import saving_utils
  from keras.saving import object_registration
  from keras.utils import io_utils
label_desc: Use image node_id 4 for calling a global function with example usage: save_subset_weights_to_hdf5_group(f, weights) without return types