          The size of the batch to be processed for each side at each iteration
      config: :class:`lib.config.FaceswapConfig`
          The configuration for this trainer
      alignments: dict
          A dictionary containing landmarks and masks if these are required for training for each
          side
      """
      def __init__(self, images, model, batch_size, config, alignments):
          logger.debug("Initializing %s: num_images: %s, batch_size: %s, config: %s)",
                       self.__class__.__name__, len(images), batch_size, config)
          self._model = model
 
</s> remove         self._config = _get_config(".".join(self.__module__.split(".")[-2:]),
                                   configfile=configfile)
 </s> add  </s> remove         The configuration `dict` generated from :file:`config.train.ini` containing the trainer \
 </s> add         The configuration `dict` generated from :file:`config.train.ini` containing the trainer </s> remove         A dictionary containing landmarks and masks if these are required for training:
 </s> add         A dictionary containing aligned face information and masks if these are required for
        training: </s> remove         * **landmarks** (`dict`, `optional`). Required if :attr:`warp_to_landmarks` is \
        ``True``. Returning dictionary has a key of **side** (`str`) the value of which is a \
        `dict` of {**filename** (`str`): **68 point landmarks** (:class:`numpy.ndarray`)}.
 </s> add         * **aligned_faces** (`dict`). Contains the aligned face information. Returning dictionary \
        has a key of **side** (`str`) the value of which is a `dict` of {**filename** (`str`): \
        :class:`lib.align.AlignedFace`}.

        * **versions** (`dict`). The Alignments file versions that the extracted faces originated \
        from for each key of **side** (`str`). Version 1.0 will be a legacy extract. Anything \
        above this will be a full-face extract
        A dictionary containing aligned face data, extract version information and masks if these
        are required for training for each side
          The size of the batch to be processed for each side at each iteration
      config: :class:`lib.config.FaceswapConfig`
          The configuration for this trainer
      alignments: dict
        A dictionary containing aligned face data, extract version information and masks if these
        are required for training for each side
        A dictionary containing aligned face data, extract version information and masks if these
        are required for training for each side
      """
      def __init__(self, images, model, batch_size, config, alignments):
          logger.debug("Initializing %s: num_images: %s, batch_size: %s, config: %s)",
                       self.__class__.__name__, len(images), batch_size, config)
          self._model = model
keep keep keep keep replace replace keep keep keep keep keep
 Extract - Increase area and move centering (#1095)

* Extract
  - Implement aligner re-feeding
  - Add extract type to pipeline.ExtractMedia
  - Add pose annotation to debug
* Convert
  - implement centering
  - remove usage of feed and reference face properties
  - Remove distributed option from convert
  - Force update of alignments file on legacy receive
* Train
  - Resize preview image to model output size
  - Force legacy centering if centering does not exist in model's state file
  - Enable training on legacy face sets

* Alignments Tool
  - Update draw to include head/pose
  - Remove DFL drop + linting
  - Remove remove-frames job
  - remove align-eyes option
  - Update legacy masks to new extract type
  - Exit if attempting to merge version 1.0 alignments files with version 2.0 alignments files
  - Re-generate thumbnails on legacy upgrade
* Mask Tool
  - Update for new extract + bugfix full frame
* Manual Tool
  - Update to new extraction method
   - Disable legacy alignments,
   - extract box bugfix
   - extract faces - size to 512 and center on head
* Preview Tool
  - Display based on model centering
* Sort Tool
  - Use alignments for sort by face

* lib.aligner
  - Add Pose Class
  - Add AlignedFace Class
  - center _MEAN_FACE on x
  - Add meta information with versioning to alignments file
  - lib.aligner.get_align_matrix to use landmarks not face
  - Refactor aligned faces in lib.faces_detect
* lib.logger
  - larger file log padding
* lib.config
  - Fix global changeable_items
* lib.face_filter
  - Use new extracted face images
* lib.image
  - bump thumbnail default size to 96px 
plugins/train/trainer/_base.py