old_contents: # Copyright 2021 The TensorFlow Authors. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  # ==============================================================================
  """Saving utilities to support Python's Pickle protocol.
  """
  
  import tensorflow.compat.v2 as tf
  
  import os
  import tarfile
  from io import BytesIO
  from uuid import uuid4
  
  from numpy import asarray
  
  from keras.saving.save import load_model
  
  
  def deserialize_model_from_bytecode(serialized_model):
    """Reconstruct a Model from the result of serialize_model_as_bytecode.
  
    Args:
        packed_keras_model (np.array): return value from serialize_model_as_bytecode.
  
    Returns:
        keras.Model: a Keras Model instance.
    """
    temp_dir = f"ram://{uuid4()}"
    b = BytesIO(serialized_model)
    with tarfile.open(fileobj=b, mode="r") as archive:
      for fname in archive.getnames():
        dest_path = os.path.join(temp_dir, fname)
        tf.io.gfile.makedirs(os.path.dirname(dest_path))
        with tf.io.gfile.GFile(dest_path, "wb") as f:
          f.write(archive.extractfile(fname).read())
    model = load_model(temp_dir)
    tf.io.gfile.rmtree(temp_dir)
    return model
  
  
  def serialize_model_as_bytecode(model):
    """Pack a Keras Model into a bytes for pickling.
  
    Args:
        model (tf.keras.Model): a Keras Model instance.
  
    Returns:
        tuple: a tuple of arguments for deserialize_from_bytecode.
    """
    temp_dir = f"ram://{uuid4()}"
    model.save(temp_dir)
    b = BytesIO()
    with tarfile.open(fileobj=b, mode="w") as archive:
      for root, _, filenames in tf.io.gfile.walk(temp_dir):
        for filename in filenames:
          dest_path = os.path.join(root, filename)
          with tf.io.gfile.GFile(dest_path, "rb") as f:
            info = tarfile.TarInfo(name=os.path.relpath(dest_path, temp_dir))
            info.size = f.size()
            archive.addfile(tarinfo=info, fileobj=f)
    tf.io.gfile.rmtree(temp_dir)
    b.seek(0)
    return (asarray(memoryview(b.read())), )
  
new_contents: # Copyright 2021 The TensorFlow Authors. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  # ==============================================================================
  """Saving utilities to support Python's Pickle protocol.
  """
  
  import tensorflow.compat.v2 as tf
  
  import os
  import tarfile
  from io import BytesIO
  from uuid import uuid4
  
  from numpy import asarray
  
  from keras.saving.save import load_model
  
  
  def deserialize_model_from_bytecode(serialized_model):
    """Reconstruct a Model from the output of `serialize_model_as_bytecode`.
  
    Args:
        serialized_model (np.array): return value from `serialize_model_as_bytecode`.
  
    Returns:
        keras.Model: Keras Model instance.
    """
    temp_dir = f"ram://{uuid4()}"
    b = BytesIO(serialized_model)
    with tarfile.open(fileobj=b, mode="r") as archive:
      for fname in archive.getnames():
        dest_path = os.path.join(temp_dir, fname)
        tf.io.gfile.makedirs(os.path.dirname(dest_path))
        with tf.io.gfile.GFile(dest_path, "wb") as f:
          f.write(archive.extractfile(fname).read())
    model = load_model(temp_dir)
    tf.io.gfile.rmtree(temp_dir)
    return model
  
  
  def serialize_model_as_bytecode(model):
    """Convert a Keras Model into a bytecode representation for pickling.
  
    Args:
        model (tf.keras.Model): Keras Model instance.
  
    Returns:
        tuple: tuple of arguments that can be sent to
            `deserialize_from_bytecode`.
    """
    temp_dir = f"ram://{uuid4()}"
    model.save(temp_dir)
    b = BytesIO()
    with tarfile.open(fileobj=b, mode="w") as archive:
      for root, _, filenames in tf.io.gfile.walk(temp_dir):
        for filename in filenames:
          dest_path = os.path.join(root, filename)
          with tf.io.gfile.GFile(dest_path, "rb") as f:
            info = tarfile.TarInfo(name=os.path.relpath(dest_path, temp_dir))
            info.size = f.size()
            archive.addfile(tarinfo=info, fileobj=f)
    tf.io.gfile.rmtree(temp_dir)
    b.seek(0)
    return (asarray(memoryview(b.read())), )
  
subject: Update pickle_utils.py