code: def levels_to_images(mlvl_tensor):
      """Concat multi-level feature maps by image.
  
      [feature_level0, feature_level1...] -> [feature_image0, feature_image1...]
      Convert the shape of each element in mlvl_tensor from (N, C, H, W) to
      (N, H*W , C), then split the element to N elements with shape (H*W, C), and
      concat elements in same image of all level along first dimension.
  
      Args:
          mlvl_tensor (list[torch.Tensor]): list of Tensor which collect from
              corresponding level. Each element is of shape (N, C, H, W)
  
      Returns:
          list[torch.Tensor]: A list that contains N tensors and each tensor is
              of shape (num_elements, C)
      """
      batch_size = mlvl_tensor[0].size(0)
      batch_list = [[] for _ in range(batch_size)]
      channels = mlvl_tensor[0].size(1)
      for t in mlvl_tensor:
          t = t.permute(0, 2, 3, 1)
          t = t.view(batch_size, -1, channels).contiguous()
          for img in range(batch_size):
              batch_list[img].append(t[img])
      return [torch.cat(item, 0) for item in batch_list]
  
file_path: insightface/detection/scrfd/mmdet/models/dense_heads/paa_head.py
imports: import numpy
  import torch
  from mmcv.runner import force_fp32
  from mmdet.core import multi_apply, multiclass_nms
  from mmdet.core.bbox.iou_calculators import bbox_overlaps
  from mmdet.models import HEADS
  from mmdet.models.dense_heads import ATSSHead
label_desc: Use image node_id 1 for calling a global function with example usage: levels_to_images(mlvl_tensor) and returns: unknown