code: def load_model(
      filepath, custom_objects=None, compile=True, safe_mode=True
  ):
      """Load a zip archive representing a Keras model."""
  
      filepath = str(filepath)
      if not filepath.endswith(".keras"):
          raise ValueError(
              "Invalid filename: expected a `.keras` extension. "
              f"Received: filepath={filepath}"
          )
  
      with file_utils.File(
          filepath, mode="r+b"
      ) as gfile_handle, zipfile.ZipFile(gfile_handle, "r") as zf:
          with zf.open(_CONFIG_FILENAME, "r") as f:
              config_json = f.read()
  
          # Note: we should NOT use a custom JSON decoder. Anything that
          # needs custom decoding must be handled in deserialize_keras_object.
          config_dict = json.loads(config_json)
          if not compile:
              # Disable compilation
              config_dict["compile_config"] = None
          # Construct the model from the configuration file in the archive.
          with ObjectSharingScope():
              model = deserialize_keras_object(
                  config_dict, custom_objects, safe_mode=safe_mode
              )
  
          all_filenames = zf.namelist()
          if _VARS_FNAME + ".h5" in all_filenames:
              weights_store = H5IOStore(
                  _VARS_FNAME + ".h5", archive=zf, mode="r"
              )
          elif _VARS_FNAME + ".npz" in all_filenames:
              weights_store = NpzIOStore(
                  _VARS_FNAME + ".npz", archive=zf, mode="r"
              )
          else:
              raise ValueError(
                  f"Expected a {_VARS_FNAME}.h5 or {_VARS_FNAME}.npz file."
              )
  
          if len(all_filenames) > 3:
              asset_store = DiskIOStore(
                  _ASSETS_DIRNAME, archive=zf, mode="r"
              )
          else:
              asset_store = None
  
          failed_trackables = set()
          error_msgs = {}
          _load_state(
              model,
              weights_store=weights_store,
              assets_store=asset_store,
              inner_path="",
              visited_trackables=set(),
              failed_trackables=failed_trackables,
              error_msgs=error_msgs,
          )
          weights_store.close()
          if asset_store:
              asset_store.close()
  
          if failed_trackables:
              _raise_loading_failure(error_msgs)
      return model
  
imports: import datetime
  import io
  import json
  import tempfile
  import warnings
  import zipfile
  import numpy
  from keras.backend.common import global_state
  from keras.layers.layer import Layer
  from keras.losses.loss import Loss
  from keras.metrics.metric import Metric
  from keras.optimizers.optimizer import Optimizer
  from keras.saving.serialization_lib import ObjectSharingScope
  from keras.saving.serialization_lib import deserialize_keras_object
  from keras.saving.serialization_lib import serialize_keras_object
  from keras.trainers.compile_utils import CompileMetrics
  from keras.utils import file_utils
  from keras.utils import naming
  from keras.version import __version__
label_desc: Use image node_id 2 for calling a global function with example usage: load_model(filepath, custom_objects, compile, safe_mode) and returns: model