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",",",")","`","."]
code: def __init__(self, data: torch.Tensor) -> None:
      if len(data) < 15:
          raise ValueError(
              f"Result must comes as vector of size(14). Got: {data.shape}."
          )
      self._data = data
  
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 1 to create a new FaceDetectorResult object  with example: obj = FaceDetectorResult(data)