code: def flatten_dense_tensors_with_padding(tensor_list, unit_size):
      num_elements = count_numel(tensor_list)
      padding = calculate_padding(num_elements, unit_size=unit_size)
  
      if padding > 0:
          pad_tensor = torch.zeros(
              padding,
              device=tensor_list[0].device,
              dtype=tensor_list[0].dtype,
          )
          padded_tensor_list = tensor_list + [pad_tensor]
      else:
          padded_tensor_list = tensor_list
  
      return flatten(padded_tensor_list)
  
file_path: colossalai/colossalai/zero/low_level/_utils.py
imports: import math
  from typing import Optional
  import torch
  import torch.distributed
  from torch._utils import _flatten_dense_tensors, _unflatten_dense_tensors
label_desc: Use image node_id 6 for calling a global function with example usage: flatten_dense_tensors_with_padding(tensor_list, unit_size) and returns: flatten