class_docstr: An abstract class representing a face detector.
  
  Any other face detection implementation must subclass it. All subclasses
  must implement ``detect_from_image``, that return a list of detected
  bounding boxes. Optionally, for speed considerations detect from path is
  recommended.
docstr: Detects faces in a given image.
  
  This function detects the faces present in a provided BGR(usually)
  image. The input can be either the image itself or the path to it.
  
  Arguments:
      tensor {torch.tensor} -- image batch tensor.
  
  Example::
  
      >>> path_to_image = 'data/image_01.jpg'
      ...   detected_faces = detect_from_image(path_to_image)
      [A list of bounding boxes (x1, y1, x2, y2)]
      >>> image = cv2.imread(path_to_image)
      ...   detected_faces = detect_from_image(image)
      [A list of bounding boxes (x1, y1, x2, y2)]
docstr_tok: ["Detects","faces","in","a","given","image",".","This","function","detects","the","faces","present","in","a","provided","BGR","(","usually",")","image",".","The","input","can","be","either","the","image","itself","or","the","path","to","it",".","Arguments",":","tensor","{","torch.tensor","}","--","image","batch","tensor",".","Example",":",":",">",">",">","path_to_image","=","'data\/image_01.jpg'","...","detected_faces","=","detect_from_image","(","path_to_image",")","[","A","list","of","bounding","boxes","(","x1",",","y1",",","x2",",","y2",")","]",">",">",">","image","=","cv2.imread","(","path_to_image",")","...","detected_faces","=","detect_from_image","(","image",")","[","A","list","of","bounding","boxes","(","x1",",","y1",",","x2",",","y2",")","]"]
code: def detect_from_batch(self, tensor):
      """Detects faces in a given image.
  
      This function detects the faces present in a provided BGR(usually)
      image. The input can be either the image itself or the path to it.
  
      Arguments:
          tensor {torch.tensor} -- image batch tensor.
  
      Example::
  
          >>> path_to_image = 'data/image_01.jpg'
          ...   detected_faces = detect_from_image(path_to_image)
          [A list of bounding boxes (x1, y1, x2, y2)]
          >>> image = cv2.imread(path_to_image)
          ...   detected_faces = detect_from_image(image)
          [A list of bounding boxes (x1, y1, x2, y2)]
  
      """
      raise NotImplementedError
  
file_path: face-alignment/face_alignment/detection/core.py
imports: import logging
  import glob
  from tqdm import tqdm
  import numpy
  import torch
  from skimage import io
label_desc: Use image node_id 3 for calling the FaceDetector obj's underlying member method code with example usage: obj.detect_from_batch(tensor) without return types