code: def test_model(model):
      x = torch.rand(2, 4, 32000)  # (batch, num_mic, length)
      num_mic = (
          torch.from_numpy(np.array([3, 2]))
          .view(
              -1,
          )
          .type(x.type())
      )  # ad-hoc array
      none_mic = torch.zeros(1).type(x.type())  # fixed-array
      y1 = model(x, num_mic.long())
      y2 = model(x, none_mic.long())
      print(y1.shape, y2.shape)
  
imports: import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from espnet2.enh.layers import dprnn
label_desc: Use image node_id 1 for calling a global function with example usage: test_model(model) without return types