(self, file_name, weights_only=False, input_signature=None)
return (self.weights, self.state)
Initializes this layer and its sublayers from a pickled checkpoint.

    In the common case (`weights_only=False`), the file must be a gziped pickled
    dictionary containing items with keys `'flat_weights', `'flat_state'` and
    `'input_signature'`, which are used to initialize this layer.
    If `input_signature` is specified, it's used instead of the one in the file.
    If `weights_only` is `True`, the dictionary does not need to have the
    `'flat_state'` item and the state it not restored either.

    Args:
      file_name: Name/path of the pickled weights/state file.
      weights_only: If `True`, initialize only the layer's weights. Else
          initialize both weights and state.
      input_signature: Input signature to be used instead of the one from file.

    Returns:
      A `(weights, state)` tuple.
Initializes this layer and its sublayers from a pickled checkpoint.
Initializes this layer and its sublayers from a pickled checkpoint .
def init_from_file(self, file_name, weights_only=False, input_signature=None):
    """Initializes this layer and its sublayers from a pickled checkpoint.

    In the common case (`weights_only=False`), the file must be a gziped pickled
    dictionary containing items with keys `'flat_weights', `'flat_state'` and
    `'input_signature'`, which are used to initialize this layer.
    If `input_signature` is specified, it's used instead of the one in the file.
    If `weights_only` is `True`, the dictionary does not need to have the
    `'flat_state'` item and the state it not restored either.

    Args:
      file_name: Name/path of the pickled weights/state file.
      weights_only: If `True`, initialize only the layer's weights. Else
          initialize both weights and state.
      input_signature: Input signature to be used instead of the one from file.

    Returns:
      A `(weights, state)` tuple.
    """
    with tf.io.gfile.GFile(file_name, 'rb') as f:
      with gzip.GzipFile(fileobj=f, compresslevel=2) as gzipf:
        dictionary = pickle.load(gzipf)
    # In the current checkpoint format, we store weights in a separate
    # non-pickled file with the same name but added ".npy".
    if isinstance(dictionary['flat_weights'], int):
      if file_name.endswith('.pkl.gz'):
        weights_path = file_name[:-6] + 'weights.npy.gz'
      else:
        weights_path = file_name + '.npy'
      if not tf.io.gfile.exists(weights_path):  # old format compatibility
        weights_path = file_name + '.npy'
      dictionary['flat_weights'] = np_from_file(
          weights_path, compresslevel=dictionary['flat_weights'])
    if input_signature is None:
      input_signature = dictionary['input_signature']
    if weights_only and input_signature is not None:
      self.init(input_signature)
    weights_and_state_sig = self.weights_and_state_signature(input_signature)
    weights, state = unflatten_weights_and_state(
        dictionary['flat_weights'], dictionary['flat_state'],
        weights_and_state_sig, weights_only=weights_only)
    if not weights_only:
      self.state = state
    self.weights = weights
    return (self.weights, self.state)
def init_from_file ( self , file_name , weights_only = False , input_signature = None ) : with tf . io . gfile . GFile ( file_name , 'rb' ) as f : with gzip . GzipFile ( fileobj = f , compresslevel = 2 ) as gzipf : dictionary = pickle . load ( gzipf ) # In the current checkpoint format, we store weights in a separate # non-pickled file with the same name but added ".npy". if isinstance ( dictionary [ 'flat_weights' ] , int ) : if file_name . endswith ( '.pkl.gz' ) : weights_path = file_name [ : - 6 ] + 'weights.npy.gz' else : weights_path = file_name + '.npy' if not tf . io . gfile . exists ( weights_path ) : # old format compatibility weights_path = file_name + '.npy' dictionary [ 'flat_weights' ] = np_from_file ( weights_path , compresslevel = dictionary [ 'flat_weights' ] ) if input_signature is None : input_signature = dictionary [ 'input_signature' ] if weights_only and input_signature is not None : self . init ( input_signature ) weights_and_state_sig = self . weights_and_state_signature ( input_signature ) weights , state = unflatten_weights_and_state ( dictionary [ 'flat_weights' ] , dictionary [ 'flat_state' ] , weights_and_state_sig , weights_only = weights_only ) if not weights_only : self . state = state self . weights = weights return ( self . weights , self . state )