code: def align_dense_tensors(tensor_list, alignment):
      num_elements = sum(t.numel() for t in tensor_list)
      remaining = num_elements % alignment
  
      if remaining:
          elements_to_add = alignment - remaining
          pad_tensor = torch.zeros(
              elements_to_add,
              device=tensor_list[0].device,
              dtype=tensor_list[0].dtype,
          )
          padded_tensor_list = tensor_list + [pad_tensor]
      else:
          padded_tensor_list = tensor_list
  
      return padded_tensor_list
  
imports: from collections.abc import Iterable
  from deepspeed.moe.utils import is_moe_param
  import os
  import psutil
  import gc
  from math import sqrt
  from math import floor
  from bisect import bisect_left
  import torch
  from torch import inf
  from deepspeed import comm
  from deepspeed.utils import groups, logger
  from deepspeed.runtime.constants import PIPE_REPLICATED
  from numpy import prod
label_desc: Use image node_id 27 for calling a global function with example usage: align_dense_tensors(tensor_list, alignment) and returns: padded_tensor_list