code: def __init__(
      self,
      allow_partial_checkpoint=False,
      experimental_io_device=None,
      experimental_skip_checkpoint=False,
      experimental_variable_policy=None,
      experimental_load_function_aliases=False,
  ):
      """Creates an object that stores options for SavedModel loading.
  
      *When to set `allow_partial_checkpoint=True`?*
  
      This can be used when loading a Keras model (`tf.keras.models.load_model`)
      with custom objects. When new variables are added to the custom object
      class, loading will fail the assertion check that all loaded variables have
      been restored, because the SavedModel checkpoint only contains the variables
      that were in original the custom object.
      See the following example:
  
      ```
      class Custom(tf.keras.Model):
        def __init__(self):
          super(Custom, self).__init__()
          self.v = tf.Variable(...)
  
        def call(self, inputs):
          return ...
  
      model = Custom()
      model.save(...)
      ```
  
      After saving, say that `Custom` is updated to include an additional
      variable.
  
      ```
      class Custom(tf.keras.Model):
        def __init__(self):
          super(Custom, self).__init__()
          self.v = tf.Variable(...)
          self.w = tf.Variable(...)
  
        def call(self, inputs):
          return ...
      ```
  
      `tf.keras.models.load_model(path, custom_objects={'Custom': Custom})` fails
      to load since `Custom.w` does not exist in the SavedModel checkpoint. To
      acknowledge that there are variables that are not restored from the
      checkpoint and successfully load the model, call:
  
      ```
      tf.keras.models.load_model(
        path, custom_objects={'Custom': Custom},
        options=tf.saved_model.LoadOptions(allow_partial_checkpoint=True))
      ```
  
      Args:
        allow_partial_checkpoint: bool. Defaults to `False`. When enabled, allows
          the SavedModel checkpoint to not entirely match the loaded object.
        experimental_io_device: string. Applies in a distributed setting.
          Tensorflow device to use to access the filesystem. If `None` (default)
          then for each variable the filesystem is accessed from the CPU:0 device
          of the host where that variable is assigned. If specified, the
          filesystem is instead accessed from that device for all variables.
          This is for example useful if you want to load from a local directory,
          such as "/tmp" when running in a distributed setting. In that case
          pass a device for the host where the "/tmp" directory is accessible.
        experimental_skip_checkpoint: bool. Defaults to `False`. If set to `True`,
          checkpoints will not be restored. Note that this in the majority of
          cases will generate an unusable model.
        experimental_variable_policy: string. The policy to apply to variables
          when loading. This is either a `saved_model.experimental.VariablePolicy`
          enum instance or one of its value strings (case is not important). See
          that enum documentation for details. A value of `None` corresponds to
          the default policy.
        experimental_load_function_aliases: bool. Defaults to `False`. If set to
          `True`, a `function_aliases` attribute will be added to the loaded
          SavedModel object.
  
      Example:
  
        load_options = tf.saved_model.LoadOptions(experimental_io_device=
          '/job:localhost')
        restoredmodel = tf.keras.models.load_model(saved_model_path,
                                                   options=load_options)
  
      """
      self.experimental_io_device = experimental_io_device
      self.allow_partial_checkpoint = allow_partial_checkpoint
      self.experimental_skip_checkpoint = experimental_skip_checkpoint
      self.experimental_variable_policy = (
          save_options.VariablePolicy.from_obj(
              experimental_variable_policy
          )
      )
      self.experimental_load_function_aliases = (
          experimental_load_function_aliases
      )
  
imports: from tensorflow.python.saved_model import save_options
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 1 to create a new LoadOptions object from inherited base classes: object with example: obj = LoadOptions(allow_partial_checkpoint, experimental_io_device, experimental_skip_checkpoint, experimental_variable_policy, experimental_load_function_aliases)