code: def test_calculate_all_attentions_MultiHeadedAttention():
      model = Dummy()
      bs = 2
      batch = {
          "x": torch.randn(bs, 3, 10),
          "x_lengths": torch.tensor([3, 2], dtype=torch.long),
          "y": torch.randn(bs, 2, 10),
          "y_lengths": torch.tensor([4, 4], dtype=torch.long),
      }
      t = calculate_all_attentions(model, batch)
      print(t)
      for k in model.desired:
          for i in range(bs):
              np.testing.assert_array_equal(
                  t[k][i].numpy(), model.desired[k][i].numpy()
              )
  
imports: from collections import defaultdict
  import numpy
  import pytest
  import torch
  from espnet2.asr.decoder.rnn_decoder import RNNDecoder
  from espnet2.main_funcs.calculate_all_attentions import calculate_all_attentions
  from espnet2.train.abs_espnet_model import AbsESPnetModel
  from espnet.nets.pytorch_backend.rnn.attentions import AttAdd
  from espnet.nets.pytorch_backend.transformer.attention import MultiHeadedAttention
label_desc: Use image node_id 1 for calling a global function with example usage: test_calculate_all_attentions_MultiHeadedAttention() without return types