docstr: Transformed label from normalized center_x, center_y, width, height to
  x_1, y_1, x_2, y_2
docstr_tok: ["Transformed","label","from","normalized","center_x",",","center_y",",","width",",","height","to","x_1",",","y_1",",","x_2",",","y_2"]
code: def __xywhn2xyxy(
      self, nlabel: torch.Tensor, width: int, height: int
  ) -> torch.Tensor:
      """
      Transformed label from normalized center_x, center_y, width, height to
      x_1, y_1, x_2, y_2
      """
      label = nlabel.clone()
      label[:, 1] = (nlabel[:, 1] - (nlabel[:, 3] / 2)) * width
      label[:, 2] = (nlabel[:, 2] - (nlabel[:, 4] / 2)) * height
      label[:, 3] = (nlabel[:, 1] + (nlabel[:, 3] / 2)) * width
      label[:, 4] = (nlabel[:, 2] + (nlabel[:, 4] / 2)) * height
  
      return label
  
imports: import os
  import warnings
  from typing import Tuple, List
  import cv2
  import numpy
  import torch
  from torch.utils.data import Dataset
  from torchvision import transforms
  from ....yolov3.utils import prepare_image
label_desc: Use image node_id 5 for calling the LoadImagesAndLabels obj's underlying member method code with example usage: obj.__xywhn2xyxy(nlabel, width, height) and returns: label