code: def convert_pt_to_tf(
      y: List[Dict[str, np.ndarray]], height: int, width: int
  ) -> List[Dict[str, np.ndarray]]:
      """
      :param y: Target values of format `List[Dict[Tensor]]`, one for each input image. The fields of the Dict are as
                follows:
  
                - boxes (FloatTensor[N, 4]): the boxes in [x1, y1, x2, y2] format, with 0 <= x1 < x2 <= W and
                                                 0 <= y1 < y2 <= H in image scale.
                - labels (Int64Tensor[N]): the labels for each image
                - scores (Tensor[N]): the scores or each prediction.
      :param height: Height of images in pixels.
      :param width: Width if images in pixels.
  
      :return: Target values of format `List[Dict[Tensor]]`, one for each input image. The fields of the Dict are as
               follows:
  
               - boxes (FloatTensor[N, 4]): the boxes in [y1, x1, y2, x2] format, with 0 <= x1 < x2 <= W and
                                            0 <= y1 < y2 <= H in scale [0, 1].
               - labels (Int64Tensor[N]): the labels for each image
               - scores (Tensor[N]): the scores or each prediction.
      """
      for i, _ in enumerate(y):
          y[i]["boxes"][:, 0] /= width
          y[i]["boxes"][:, 1] /= height
          y[i]["boxes"][:, 2] /= width
          y[i]["boxes"][:, 3] /= height
  
          y[i]["boxes"] = y[i]["boxes"][:, [1, 0, 3, 2]]
  
          y[i]["labels"] = y[i]["labels"] - 1
  
      return y
  
imports: from typing import Dict, List, Union, Tuple, Optional, TYPE_CHECKING
  import numpy
label_desc: Use image node_id 2 for calling a global function with example usage: convert_pt_to_tf(y, height, width) and returns: y