docstr: This function is used to transpose image first training targets to level first ones
  :return: level first training targets
docstr_tok: ["This","function","is","used","to","transpose","image","first","training","targets","to","level","first","ones",":","return",":","level","first","training","targets"]
code: def _transpose(self, training_targets, num_loc_list):
      """
      This function is used to transpose image first training targets to level first ones
      :return: level first training targets
      """
      for im_i in range(len(training_targets)):
          training_targets[im_i] = torch.split(
              training_targets[im_i], num_loc_list, dim=0
          )
  
      targets_level_first = []
      for targets_per_level in zip(*training_targets):
          targets_level_first.append(
              torch.cat(targets_per_level, dim=0)
          )
      return targets_level_first
  
imports: import logging
  import math
  from typing import List, Dict
  import torch
  from torch import nn
  from torch.nn import functional
  from ...nn.shape_spec import ShapeSpec
  from ...nn.deform_conv import DeformConvWithChangeableStride
  from ...nn.group_norm import NaiveGroupNorm
  from ...data.structures import Instances, Boxes
  from ...nn.focal_loss import sigmoid_focal_loss_jit
  from ...nn.nms import ml_nms
  from ...utils.loss import IOULoss
  from ...utils.comm import get_world_size, reduce_sum, compute_locations
label_desc: Use image node_id 2 for calling the FCOSOutputs obj's underlying member method code with example usage: obj._transpose(training_targets, num_loc_list) and returns: targets_level_first