              original values you should take a copy prior to running this method
          method: ['single','centroid','median','ward']
              The clustering method to use.
  
          Returns
          -------
          list:
              List of indices with the order implied by the hierarchical tree
          """
          logger.info("Sorting face distances. Depending on your dataset this may take some time...")
          num_predictions, dims = predictions.shape
  
          kwargs = dict(method=method)
          if self._use_vector_linkage(num_predictions, dims):
              func = linkage_vector
          else:
 
</s> remove         kwargs = dict(method=method)
        if self._use_vector_linkage(num_predictions, dims):
            func = linkage_vector
        else:
            kwargs["preserve_input"] = False
            func = linkage
 </s> add         self._result_linkage = self._do_linkage(predictions, method)
        logger.debug("Initialized %s", self.__class__.__name__) </s> remove         Parameters
        ----------
        predictions: numpy.ndarray
            A stacked matrix of vgg_face2 predictions of the shape (`N`, `D`) where `N` is the
            number of observations and `D` are the number of dimensions.  NB: The given
            :attr:`predictions` will be overwritten to save memory. If you still require the
            original values you should take a copy prior to running this method
        method: ['single','centroid','median','ward']
            The clustering method to use.
 </s> add     def __init__(self,
                 predictions: np.ndarray,
                 method: Literal["single", "centroid", "median", "ward"],
                 threshold: Optional[float] = None) -> None:
        logger.debug("Initializing: %s (predictions: %s, method: %s, threshold: %s)",
                     self.__class__.__name__, predictions.shape, method, threshold)
        self._num_predictions = predictions.shape[0] </s> remove         Transforms a distance matrix into a sorted distance matrix according to the order implied
        by the hierarchical tree (dendrogram).
 </s> add class Cluster():  # pylint: disable=too-few-public-methods
    """ Cluster the outputs from a VGG-Face 2 Model

    Parameters
    ----------
    predictions: numpy.ndarray
        A stacked matrix of vgg_face2 predictions of the shape (`N`, `D`) where `N` is the
        number of observations and `D` are the number of dimensions.  NB: The given
        :attr:`predictions` will be overwritten to save memory. If you still require the
        original values you should take a copy prior to running this method
    method: ['single','centroid','median','ward']
        The clustering method to use.
    threshold: float, optional
        The threshold to start creating bins for. Set to ``None`` to disable binning
    """ </s> remove     def _seriation(self, tree, points, current_index):
 </s> add     def _do_linkage(self,
                    predictions: np.ndarray,
                    method: Literal["single", "centroid", "median", "ward"]) -> np.ndarray:
        """ Use FastCluster to perform vector or standard linkage

        Parameters
        ----------
        predictions: :class:`numpy.ndarray`
            A stacked matrix of vgg_face2 predictions of the shape (`N`, `D`) where `N` is the
            number of observations and `D` are the number of dimensions.
        method: ['single','centroid','median','ward']
            The clustering method to use.

        Returns
        -------
        :class:`numpy.ndarray`
            The [`num_predictions`, 4] linkage vector
        """
        dims = predictions.shape[-1]
        if self._use_vector_linkage(dims):
            retval = linkage_vector(predictions, method=method)
        else:
            retval = linkage(predictions, method=method, preserve_input=False)
        logger.debug("Linkage shape: %s", retval.shape)
        return retval

    def _process_leaf_node(self,
                           current_index: int,
                           current_bin: int) -> List[Tuple[int, int]]:
        """ Process the output when we have hit a leaf node """
        if not self._should_output_bins:
            return [(current_index, 0)]

        if current_bin not in self._bins:
            next_val = 0 if not self._bins else max(self._bins.values()) + 1
            self._bins[current_bin] = next_val
        return [(current_index, self._bins[current_bin])]

    def _get_bin(self,
                 tree: np.ndarray,
                 points: int,
                 current_index: int,
                 current_bin: int) -> int:
        """ Obtain the bin that we are currently in.

        If we are not currently below the threshold for binning, get a new bin ID from the integer
        iterator.

        Parameters
        ----------
        tree: numpy.ndarray
           A hierarchical tree (dendrogram)
        points: int
            The number of points given to the clustering process
        current_index: int
            The position in the tree for the recursive traversal
        current_bin int, optional
            The ID for the bin we are currently in. Only used when binning is enabled

        Returns
        -------
        int
            The current bin ID for the node
        """
        if tree[current_index - points, 2] >= self._threshold:
            current_bin = next(self._iterator)
            logger.debug("Creating new bin ID: %s", current_bin)
        return current_bin

    def _seriation(self,
                   tree: np.ndarray,
                   points: int,
                   current_index: int,
                   current_bin: int = 0) -> List[Tuple[int, int]]:
        self._should_output_bins = threshold is not None
        self._threshold = 0.0 if threshold is None else threshold
        self._bins: Dict[int, int] = {}
        self._iterator = self._integer_iterator()
              original values you should take a copy prior to running this method
          method: ['single','centroid','median','ward']
              The clustering method to use.
  
        self._should_output_bins = threshold is not None
        self._threshold = 0.0 if threshold is None else threshold
        self._bins: Dict[int, int] = {}
        self._iterator = self._integer_iterator()
        self._should_output_bins = threshold is not None
        self._threshold = 0.0 if threshold is None else threshold
        self._bins: Dict[int, int] = {}
        self._iterator = self._integer_iterator()
        self._should_output_bins = threshold is not None
        self._threshold = 0.0 if threshold is None else threshold
        self._bins: Dict[int, int] = {}
        self._iterator = self._integer_iterator()
        self._should_output_bins = threshold is not None
        self._threshold = 0.0 if threshold is None else threshold
        self._bins: Dict[int, int] = {}
        self._iterator = self._integer_iterator()
        self._should_output_bins = threshold is not None
        self._threshold = 0.0 if threshold is None else threshold
        self._bins: Dict[int, int] = {}
        self._iterator = self._integer_iterator()
        self._should_output_bins = threshold is not None
        self._threshold = 0.0 if threshold is None else threshold
        self._bins: Dict[int, int] = {}
        self._iterator = self._integer_iterator()
        self._should_output_bins = threshold is not None
        self._threshold = 0.0 if threshold is None else threshold
        self._bins: Dict[int, int] = {}
        self._iterator = self._integer_iterator()
  
          kwargs = dict(method=method)
          if self._use_vector_linkage(num_predictions, dims):
              func = linkage_vector
          else:
keep keep keep keep replace replace replace replace replace replace replace keep keep keep keep keep
 Overhaul sort:
  - Standardize image data reading and writing
  - Optimize loading (just one pass required)
  - Make all sort groups binnable (to greater or lesser results)
  - Add sort by pitch
  - Deprecate multiple options
  - linting, docs + locales 
plugins/extract/recognition/vgg_face2_keras.py