code: def test_transpose_attention(self):
      feature_dim = 8
      seq_len = 10
      fake_input = torch.rand([4, seq_len, feature_dim])
      mask = _lengths2mask(
          torch.tensor([seq_len, seq_len, seq_len, seq_len]),
          fake_input.shape[1],
      )
      model = TransposeMultiheadAttention(feature_dim, num_heads=2)
      output = model(fake_input, mask=mask)
      self.assertTrue(output.shape, fake_input.shape)
  
file_path: pytorchvideo/tests/test_models_masked_multistream.py
imports: import copy
  import unittest
  import torch
  import torch.nn
  from pytorchvideo.layers import make_multilayer_perceptron, PositionalEncoding
  from pytorchvideo.models.masked_multistream import LearnMaskedDefault, LSTM, MaskedSequential, MaskedTemporalPooling, TransposeMultiheadAttention, TransposeTransformerEncoder
label_desc: Use image node_id 4 for calling the TestMaskedMultiStream obj's underlying member method code with example usage: obj.test_transpose_attention() without return types