code: def load_weights_from_hdf5_group(f, model):
      """Implements topological (order-based) weight loading.
  
      Args:
          f: A pointer to a HDF5 group.
          model: Model instance.
  
      Raises:
          ValueError: in case of mismatch between provided layers
              and weights file.
      """
      if "keras_version" in f.attrs:
          original_keras_version = f.attrs["keras_version"]
          if hasattr(original_keras_version, "decode"):
              original_keras_version = original_keras_version.decode(
                  "utf8"
              )
      else:
          original_keras_version = "1"
      if "backend" in f.attrs:
          original_backend = f.attrs["backend"]
          if hasattr(original_backend, "decode"):
              original_backend = original_backend.decode("utf8")
      else:
          original_backend = None
  
      filtered_layers = []
      for layer in model.layers:
          weights = _legacy_weights(layer)
          if weights:
              filtered_layers.append(layer)
  
      layer_names = load_attributes_from_hdf5_group(f, "layer_names")
      filtered_layer_names = []
      for name in layer_names:
          g = f[name]
          weight_names = load_attributes_from_hdf5_group(
              g, "weight_names"
          )
          if weight_names:
              filtered_layer_names.append(name)
      layer_names = filtered_layer_names
      if len(layer_names) != len(filtered_layers):
          raise ValueError(
              "Layer count mismatch when loading weights from file. "
              f"Model expected {len(filtered_layers)} layers, found "
              f"{len(layer_names)} saved layers."
          )
  
      for k, name in enumerate(layer_names):
          g = f[name]
          layer = filtered_layers[k]
          symbolic_weights = _legacy_weights(layer)
          weight_values = load_subset_weights_from_hdf5_group(g)
          if len(weight_values) != len(symbolic_weights):
              raise ValueError(
                  f"Weight count mismatch for layer #{k} (named {layer.name} in "
                  f"the current model, {name} in the save file). "
                  f"Layer expects {len(symbolic_weights)} weight(s). Received "
                  f"{len(weight_values)} saved weight(s)"
              )
          for ref_v, val in zip(symbolic_weights, weight_values):
              ref_v.assign(val)
  
      if "top_level_model_weights" in f:
          symbolic_weights = list(
              # model.weights
              v
              for v in model._trainable_variables
              + model._non_trainable_variables
              if v in model.weights
          )
          weight_values = load_subset_weights_from_hdf5_group(
              f["top_level_model_weights"]
          )
          if len(weight_values) != len(symbolic_weights):
              raise ValueError(
                  "Weight count mismatch for top-level weights when loading "
                  "weights from file. "
                  f"Model expects {len(symbolic_weights)} top-level weight(s). "
                  f"Received {len(weight_values)} saved top-level weight(s)"
              )
          for ref_v, val in zip(symbolic_weights, weight_values):
              ref_v.assign(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 7 for calling a global function with example usage: load_weights_from_hdf5_group(f, model) without return types