          >>>         extractor.input_queue.put({"filename": "path/to/image/file",
          >>>                                    "image": np.array(image),
          >>>                                    "detected_faces": [<DetectedFace objects]})
          """
          retval = self._is_parallel or self.phase == "mask"
          logger.trace(retval)
          return retval
  
      def set_batchsize(self, plugin_type, batchsize):
          """ Set the batchsize of a given :attr:`plugin_type` to the given :attr:`batchsize`.
 
</s> remove         >>>                                    "image": np.array(image),
 </s> add         >>>                                    "image": numpy.array(image), </s> remove         >>>                                    "image": np.array(image),
 </s> add         >>>                                    "image": numpy.array(image), </s> remove         retval = 1 if self._is_parallel else 3
 </s> add         retval = 1 if self._is_parallel else len(self._flow) </s> remove         """ Set the batchsize of a given :attr:`plugin_type` to the given :attr:`batchsize`.
 </s> add         """ Set the batch size of a given :attr:`plugin_type` to the given :attr:`batchsize`. </s> remove         >>>                                    "image": np.array(image)})
 </s> add         >>>                                    "image": numpy.array(image)})
        retval = self._is_parallel or self.phase == self._final_phase
          >>>         extractor.input_queue.put({"filename": "path/to/image/file",
          >>>                                    "image": np.array(image),
          >>>                                    "detected_faces": [<DetectedFace objects]})
          """
        retval = self._is_parallel or self.phase == self._final_phase
          logger.trace(retval)
          return retval
  
      def set_batchsize(self, plugin_type, batchsize):
          """ Set the batchsize of a given :attr:`plugin_type` to the given :attr:`batchsize`.
keep keep keep keep replace keep keep keep keep keep
 Smart Mask - Extract code review

- Lint simple_tests.py
- Only reformat alignments file if it exists otherwise change filename
- Update legacy alignments to new format at all stages
- faces_detect.Mask.from_dict - logging format fix
- convert.py fix otf for new pipeline
- cli.py - Add note that masks not used. Revert convert masks
- faces_detect.py - Revert non-extract code
- Add .p and .pickle extensions for serializer
- plugins/extract revert some changes
- scripts/fsmedia - Revert code changes
- Pipeline - cleanup
- Consistant alpha channel stripping (fixes single-process)
- Store landmarks as numpy array
- Code attribution
- Normalize feed face and reference face to 0.0 - 1.0 in convert
- Lock in mask VRAM sized
- Add documentation to plugin_loader
- Update alignments tool to work with new format 
plugins/extract/pipeline.py