code: def mass_perservation_formula(
      alpha, left_padding=False, padding_mask=None
  ):
      if padding_mask is None or alpha.size(-1) == 1:
          if alpha.size(-1) > 1:
              alpha[:, :, -1] = 1 - alpha[:, :, :-1].sum(dim=-1)
          return alpha
  
      src_lens = (padding_mask.logical_not()).sum(dim=1).long()
  
      bsz, tgt_len, src_len = alpha.size()
  
      assert not left_padding or (
          left_padding and (not padding_mask[:, 0].any())
      )
  
      alpha = alpha.masked_fill(padding_mask.unsqueeze(1), 0)
  
      for bsz_i in range(bsz):
          if left_padding:
              alpha[bsz_i, :, -1] = 1 - alpha[bsz_i, :, :-1].sum(dim=-1)
          else:
              alpha[bsz_i, :, src_lens[bsz_i] - 1] = 1 - alpha[
                  bsz_i, :, : src_lens[bsz_i] - 1
              ].sum(dim=-1)
  
      return alpha
  
imports: import argparse
  import unittest
  from typing import Any, Dict
  import torch
  from examples.simultaneous_translation.models import transformer_monotonic_attention
  from tests.test_roberta import FakeTask
label_desc: Use image node_id 5 for calling a global function with example usage: mass_perservation_formula(alpha, left_padding, padding_mask) and returns: alpha, alpha