code: def test_RNN(device):
      from speechbrain.nnet.RNN import (
          RNN,
          GRU,
          LSTM,
          LiGRU,
          QuasiRNN,
          RNNCell,
      )
  
      # Check RNN
      inputs = torch.randn(4, 2, 7, device=device)
      net = RNN(
          hidden_size=5,
          input_shape=inputs.shape,
          num_layers=2,
          bidirectional=False,
      ).to(device)
      output, hn = net(inputs)
      output_l = []
      hn_t = None
      for t in range(inputs.shape[1]):
          out_t, hn_t = net(inputs[:, t, :].unsqueeze(1), hn_t)
          output_l.append(out_t.squeeze(1))
  
      out_steps = torch.stack(output_l, dim=1)
      assert torch.all(
          torch.lt(torch.add(out_steps, -output), 1e-3)
      ), "GRU output mismatch"
      assert torch.all(
          torch.lt(torch.add(hn_t, -hn), 1e-3)
      ), "GRU hidden states mismatch"
      assert torch.jit.trace(net, inputs)
  
      # Check GRU
      inputs = torch.randn(4, 2, 7, device=device)
      net = GRU(
          hidden_size=5,
          input_shape=inputs.shape,
          num_layers=2,
          bidirectional=False,
      ).to(device)
      output, hn = net(inputs)
      output_l = []
      hn_t = None
      for t in range(inputs.shape[1]):
          out_t, hn_t = net(inputs[:, t, :].unsqueeze(1), hn_t)
          output_l.append(out_t.squeeze(1))
  
      out_steps = torch.stack(output_l, dim=1)
      assert torch.all(
          torch.lt(torch.add(out_steps, -output), 1e-3)
      ), "GRU output mismatch"
      assert torch.all(
          torch.lt(torch.add(hn_t, -hn), 1e-3)
      ), "GRU hidden states mismatch"
      assert torch.jit.trace(net, inputs)
  
      # Check LSTM
      inputs = torch.randn(4, 2, 7, device=device)
      net = LSTM(
          hidden_size=5,
          input_shape=inputs.shape,
          num_layers=2,
          bidirectional=False,
      ).to(device)
      output, hn = net(inputs)
      output_l = []
      hn_t = None
      for t in range(inputs.shape[1]):
          out_t, hn_t = net(inputs[:, t, :].unsqueeze(1), hn_t)
          output_l.append(out_t.squeeze(1))
  
      out_steps = torch.stack(output_l, dim=1)
      assert torch.all(
          torch.lt(torch.add(out_steps, -output), 1e-3)
      ), "LSTM output mismatch"
      assert torch.all(
          torch.lt(torch.add(hn_t[0], -hn[0]), 1e-3)
      ) and torch.all(
          torch.lt(torch.add(hn_t[1], -hn[1]), 1e-3)
      ), "LSTM hidden states mismatch"
      assert torch.jit.trace(net, inputs)
  
      # Check LiGRU
      inputs = torch.randn(1, 2, 2, device=device)
      net = LiGRU(
          hidden_size=5,
          input_shape=inputs.shape,
          num_layers=2,
          bidirectional=False,
          normalization="layernorm",
      ).to(device)
  
      output, hn = net(inputs)
      output_l = []
      hn_t = None
      for t in range(inputs.shape[1]):
          out_t, hn_t = net(inputs[:, t, :].unsqueeze(1), hn_t)
          output_l.append(out_t.squeeze(1))
  
      out_steps = torch.stack(output_l, dim=1)
  
      assert torch.all(
          torch.lt(torch.add(out_steps, -output), 1e-3)
      ), "LiGRU output mismatch"
      assert torch.all(
          torch.lt(torch.add(hn_t[0], -hn[0]), 1e-3)
      ) and torch.all(
          torch.lt(torch.add(hn_t[1], -hn[1]), 1e-3)
      ), "LiGRU hidden states mismatch"
  
      # Check QuasiRNN
      inputs = torch.randn(1, 2, 2, device=device)
      net = QuasiRNN(
          hidden_size=5,
          input_shape=inputs.shape,
          num_layers=2,
          bidirectional=False,
      ).to(device)
  
      output, hn = net(inputs)
      output_l = []
      hn_t = None
      for t in range(inputs.shape[1]):
          out_t, hn_t = net(inputs[:, t, :].unsqueeze(1), hn_t)
          output_l.append(out_t.squeeze(1))
  
      out_steps = torch.stack(output_l, dim=1)
  
      assert torch.all(
          torch.lt(torch.add(out_steps, -output), 1e-3)
      ), "QuasiRNN output mismatch"
      assert torch.all(
          torch.lt(torch.add(hn_t[0], -hn[0][1]), 1e-3)
      ) and torch.all(
          torch.lt(torch.add(hn_t[1], -hn[1][1]), 1e-3)
      ), "QuasiRNN hidden states mismatch"
      assert torch.jit.trace(net, inputs)
  
      # Check RNNCell
      inputs = torch.randn(4, 2, 7, device=device)
      net = RNNCell(
          hidden_size=5, input_size=7, num_layers=2, dropout=0.0
      ).to(device)
      hn_t = None
      output_lst = []
      for t in range(inputs.shape[1]):
          output, hn_t = net(inputs[:, t], hn_t)
          output_lst.append(output)
  
      out_steps = torch.stack(output_lst, dim=1)
      rnn = torch.nn.RNN(
          input_size=7, hidden_size=5, num_layers=2, batch_first=True
      ).to(device)
  
      # rename the state_dict
      state = net.state_dict()
      new_state = []
      for name, tensor in state.items():
          index, weight_id = (
              name[len("rnn_cells.")],
              name[len("rnn_cells.0.") :],
          )
          new_state.append((f"{weight_id}_l{index}", tensor))
      new_state = OrderedDict(new_state)
      rnn.load_state_dict(new_state)
      output, hn = rnn(inputs)
  
      assert torch.all(
          torch.lt(torch.add(out_steps, -output), 1e-3)
      ), "RNNCell output mismatch"
      assert torch.all(
          torch.lt(torch.add(hn_t[0], -hn[0]), 1e-3)
      ) and torch.all(
          torch.lt(torch.add(hn_t[1], -hn[1]), 1e-3)
      ), "RNNCell hidden states mismatch"
      assert torch.jit.trace(rnn, inputs)
  
imports: import torch
  import torch.nn
  from collections import OrderedDict
label_desc: Use image node_id 1 for calling a global function with example usage: test_RNN(device) without return types