code: def generate_path_test():
      durations = T.randint(1, 4, (10, 21))
      x_length = T.randint(18, 22, (10,))
      x_mask = sequence_mask(x_length).unsqueeze(1).long()
      durations = durations * x_mask.squeeze(1)
      y_length = durations.sum(1)
      y_mask = sequence_mask(y_length).unsqueeze(1).long()
      attn_mask = (
          (T.unsqueeze(x_mask, -1) * T.unsqueeze(y_mask, 2))
          .squeeze(1)
          .long()
      )
      print(attn_mask.shape)
      path = generate_path(durations, attn_mask)
      assert path.shape == (10, 21, durations.sum(1).max().item())
      for b in range(durations.shape[0]):
          current_idx = 0
          for t in range(durations.shape[1]):
              assert all(
                  path[
                      b,
                      t,
                      current_idx : current_idx
                      + durations[b, t].item(),
                  ]
                  == 1.0
              )
              assert all(path[b, t, :current_idx] == 0.0)
              assert all(
                  path[b, t, current_idx + durations[b, t].item() :]
                  == 0.0
              )
              current_idx += durations[b, t].item()
  
imports: import torch
  from TTS.tts.utils.helpers import average_over_durations, generate_path, rand_segments, segment, sequence_mask
label_desc: Use image node_id 5 for calling a global function with example usage: generate_path_test() without return types