code: def test_calculate_all_attentions(atype):
      model = Dummy2(atype)
      bs = 2
      batch = {
          "x": torch.randn(bs, 20, 128),
          "x_lengths": torch.tensor([20, 17], dtype=torch.long),
          "y": torch.randint(0, 50, [bs, 7]),
          "y_lengths": torch.tensor([7, 5], dtype=torch.long),
      }
      t = calculate_all_attentions(model, batch)
      for k, o in t.items():
          for i, att in enumerate(o):
              print(att.shape)
              if att.dim() == 2:
                  att = att[None]
              for a in att:
                  assert a.shape == (
                      batch["y_lengths"][i],
                      batch["x_lengths"][i],
                  )
  
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 2 for calling a global function with example usage: test_calculate_all_attentions(atype) without return types