class_docstr: Detect faces in a given image using a CNN.
  
  By default, it uses the method described in :cite:`facedetect-yu`.
  
  Args:
      top_k: the maximum number of detections to return before the nms.
      confidence_threshold: the threshold used to discard detections.
      nms_threshold: the threshold used by the nms for iou.
      keep_top_k: the maximum number of detections to return after the nms.
  
  Return:
      A list of B tensors with shape :math:`(N,15)` to be used with :py:class:`kornia.contrib.FaceDetectorResult`.
  
  Example:
      >>> img = torch.rand(1, 3, 320, 320)
      >>> detect = FaceDetector()
      >>> res = detect(img)
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docstr: Detect faces in a given batch of images.
  
  Args:
      image: batch of images :math:`(B,3,H,W)`
  
  Return:
      List[torch.Tensor]: list with the boxes found on each image. :math:`Bx(N,15)`.
docstr_tok: ["Detect","faces","in","a","given","batch","of","images",".","Args",":","image",":","batch","of","images",":","math",":","`","(","B,3",",","H",",","W",")","`","Return",":","List","[","torch.Tensor","]",":","list","with","the","boxes","found","on","each","image",".",":","math",":","`","Bx","(","N,15",")","`","."]
code: def forward(self, image: torch.Tensor) -> List[torch.Tensor]:
      r"""Detect faces in a given batch of images.
  
      Args:
          image: batch of images :math:`(B,3,H,W)`
  
      Return:
          List[torch.Tensor]: list with the boxes found on each image. :math:`Bx(N,15)`.
      """
      img = self.preprocess(image)
      out = self.model(img)
      return self.postprocess(out, img.shape[-2], img.shape[-1])
  
imports: import math
  from enum import Enum
  from typing import Dict, List, Optional, Tuple
  import torch
  import torch.nn.functional
  from torch import nn
  from kornia.geometry.bbox import nms
  from kornia.utils.helpers import map_location_to_cpu
label_desc: Use image node_id 4 for calling the FaceDetector obj's underlying member method code with example usage: obj.forward(image) and returns: self