class_docstr: Encapsulate the results obtained by the :py:class:`kornia.contrib.FaceDetector`.
  
  Args:
      data: the encoded results coming from the feature detector with shape :math:`(14,)`.
class_docstr_tok: ["Encapsulate","the","results","obtained","by","the",":","py",":","class",":","`","kornia.contrib.FaceDetector","`",".","Args",":","data",":","the","encoded","results","coming","from","the","feature","detector","with","shape",":","math",":","`","(","14",",",")","`","."]
docstr: The [x y] position of a given facial keypoint.
  
  Args:
      keypoint: the keypoint type to return the position.
docstr_tok: ["The","[","x","y","]","position","of","a","given","facial","keypoint",".","Args",":","keypoint",":","the","keypoint","type","to","return","the","position","."]
code: def get_keypoint(self, keypoint: FaceKeypoint) -> torch.Tensor:
      """The [x y] position of a given facial keypoint.
  
      Args:
          keypoint: the keypoint type to return the position.
      """
      if keypoint == FaceKeypoint.EYE_LEFT:
          out = self._data[..., (4, 5)]
      elif keypoint == FaceKeypoint.EYE_RIGHT:
          out = self._data[..., (6, 7)]
      elif keypoint == FaceKeypoint.NOSE:
          out = self._data[..., (8, 9)]
      elif keypoint == FaceKeypoint.MOUTH_LEFT:
          out = self._data[..., (10, 11)]
      elif keypoint == FaceKeypoint.MOUTH_RIGHT:
          out = self._data[..., (12, 13)]
      else:
          raise ValueError(f"Not valid keypoint type. Got: {keypoint}.")
      return out
  
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 7 for calling the FaceDetectorResult obj's underlying member method code with example usage: obj.get_keypoint(keypoint) and returns: out