code: def _pad_to_largest_tensor(tensor, group):
      """
      Padding all the tensors from different GPUs to the largest ones.
      Args:
          tensor (tensor): tensor to pad.
          group (group): pytorch dist group.
      Returns:
          list[int]: size of the tensor, on each rank
          Tensor: padded tensor that has the max size
      """
      world_size = dist.get_world_size(group=group)
      assert (
          world_size >= 1
      ), "comm.gather/all_gather must be called from ranks within the given group!"
      local_size = torch.tensor(
          [tensor.numel()], dtype=torch.int64, device=tensor.device
      )
      size_list = [
          torch.zeros([1], dtype=torch.int64, device=tensor.device)
          for _ in range(world_size)
      ]
      dist.all_gather(size_list, local_size, group=group)
      size_list = [int(size.item()) for size in size_list]
  
      max_size = max(size_list)
  
      # we pad the tensor because torch all_gather does not support
      # gathering tensors of different shapes
      if local_size != max_size:
          padding = torch.zeros(
              (max_size - local_size,),
              dtype=torch.uint8,
              device=tensor.device,
          )
          tensor = torch.cat((tensor, padding), dim=0)
      return size_list, tensor
  
imports: import functools
  import logging
  import pickle
  import torch
  import torch.distributed
  from pytorchvideo.layers.distributed import cat_all_gather, get_local_process_group, get_local_rank, get_local_size, get_world_size, init_distributed_training
label_desc: Use image node_id 11 for calling a global function with example usage: _pad_to_largest_tensor(tensor, group) and returns: size_list,  tensor