code: def test_expected_alignment(self):
      for longer_src in [True, False]:
          sample = make_sample_with_padding(longer_src)
          _, extra_out = self.model.forward(**sample["net_input"])
          for item in extra_out.attn_list:
              p_choose = item["p_choose"]
              alpha_system = item["alpha"]
              self.assertTrue(p_choose.size() == alpha_system.size())
              bsz, num_head, tgt_len, src_len = alpha_system.size()
              alpha_system = alpha_system.view(-1, tgt_len, src_len)
              p_choose = p_choose.view(-1, tgt_len, src_len)
  
              alpha_real = expected_alignment_formula(
                  p_choose,
                  self.model.decoder.layers[
                      0
                  ].encoder_attn.mass_preservation,
                  sample["net_input"]["src_tokens"].eq(PAD_INDEX),
              )
  
              self.assertTrue(
                  torch.abs(alpha_system - alpha_real).le(5e-5).all(),
              )
  
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 3 for calling the MonotonicAttentionTestAbstractClass obj's underlying member method code with example usage: obj.test_expected_alignment() without return types