code: def _onnx_nested_tensor_from_tensor_list(
      tensor_list: List[Tensor],
  ) -> NestedTensor:
      max_size = []
      for i in range(tensor_list[0].dim()):
          max_size_i = torch.max(
              torch.stack([img.shape[i] for img in tensor_list]).to(
                  torch.float32
              )
          ).to(torch.int64)
          max_size.append(max_size_i)
      max_size = tuple(max_size)
  
      # work around for
      # pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img)
      # m[: img.shape[1], :img.shape[2]] = False
      # which is not yet supported in onnx
      padded_imgs = []
      padded_masks = []
      for img in tensor_list:
          padding = [
              (s1 - s2) for s1, s2 in zip(max_size, tuple(img.shape))
          ]
          padded_img = torch.nn.functional.pad(
              img, (0, padding[2], 0, padding[1], 0, padding[0])
          )
          padded_imgs.append(padded_img)
  
          m = torch.zeros_like(
              img[0], dtype=torch.int, device=img.device
          )
          padded_mask = torch.nn.functional.pad(
              m, (0, padding[2], 0, padding[1]), "constant", 1
          )
          padded_masks.append(padded_mask.to(torch.bool))
  
      tensor = torch.stack(padded_imgs)
      mask = torch.stack(padded_masks)
  
      return NestedTensor(tensor, mask=mask)
  
imports: import os
  import subprocess
  import time
  from collections import defaultdict, deque
  import datetime
  import pickle
  from packaging import version
  from typing import Optional, List
  import torch
  import torch.distributed
  from torch import Tensor
  import torchvision
label_desc: Use image node_id 7 for calling a global function with example usage: _onnx_nested_tensor_from_tensor_list(tensor_list) and returns: NestedTensor