code: def __init__(
      self,
      device,
      back_model=False,
      path_to_detector=None,
      path_to_anchor=None,
      verbose=False,
      min_score_thresh=0.5,
      min_suppression_threshold=0.3,
  ):
      super(BlazeFaceDetector, self).__init__(device, verbose)
  
      # Initialise the face detector
      self.back_model = back_model
      if path_to_detector is None:
          if back_model:
              model_weights = load_url(
                  models_urls["blazeface_back_weights"]
              )
              model_anchors = np.load(
                  load_file_from_url(
                      models_urls["blazeface_back_anchors"]
                  )
              )
          else:
              model_weights = load_url(models_urls["blazeface_weights"])
              model_anchors = np.load(
                  load_file_from_url(models_urls["blazeface_anchors"])
              )
      else:
          model_weights = torch.load(path_to_detector)
          model_anchors = np.load(path_to_anchor)
  
      self.face_detector = BlazeFace(back_model=back_model)
      self.face_detector.load_state_dict(model_weights)
      self.face_detector.load_anchors_from_npy(model_anchors, device)
  
      # Optionally change the thresholds:
      self.face_detector.min_score_thresh = min_score_thresh
      self.face_detector.min_suppression_threshold = (
          min_suppression_threshold
      )
  
      self.face_detector.to(device)
      self.face_detector.eval()
  
file_path: face-alignment/face_alignment/detection/blazeface/blazeface_detector.py
imports: from torch.utils.model_zoo import load_url
  from ..core import FaceDetector
  from ...utils import load_file_from_url
  from .net_blazeface import BlazeFace
  from .detect import *
label_desc: Use image node_id 1 to create a new BlazeFaceDetector object from inherited base classes: FaceDetector with example: obj = BlazeFaceDetector(device, back_model, path_to_detector, path_to_anchor, verbose, min_score_thresh, min_suppression_threshold)