code: def _transpose(
      tensor_list: List[Tensor], num_point_list: list
  ) -> List[Tensor]:
      """This function is used to transpose image first tensors to level first
      ones."""
      for img_idx in range(len(tensor_list)):
          tensor_list[img_idx] = torch.split(
              tensor_list[img_idx], num_point_list, dim=0
          )
  
      tensors_level_first = []
      for targets_per_level in zip(*tensor_list):
          tensors_level_first.append(
              torch.cat(targets_per_level, dim=0)
          )
      return tensors_level_first
  
imports: from typing import Dict, List, Optional, Sequence, Tuple
  import torch
  import torch.nn
  from mmcv.cnn import Scale
  from mmengine.structures import InstanceData
  from torch import Tensor
  from mmdet.registry import MODELS
  from mmdet.structures.bbox import bbox2distance
  from mmdet.utils import ConfigType, InstanceList, OptConfigType, OptInstanceList, reduce_mean
  from ..utils import multi_apply
  from .anchor_free_head import AnchorFreeHead
label_desc: Use image node_id 1 for calling a global function with example usage: _transpose(tensor_list, num_point_list) and returns: tensors_level_first