code: def benchmark_gru_gpu():
      print("-" * 60)
      print("GRU GPU benchmark:")
  
      rnn = nn.GRU(
          input_size=input_size,
          hidden_size=hidden_size,
          batch_first=False,
          bidirectional=(n_directions == 2),
          num_layers=1,
      ).cuda()
  
      input = torch.randn(seq_len, batch_size, input_size).cuda()
      h0 = torch.randn(
          n_layers * n_directions, batch_size, hidden_size
      ).cuda()
      print("input.shape", input.shape)
      print("h0.shape", h0.shape)
      output, hn = rnn(input, h0)
      print("output.shape", output.shape)
      print("hn.shape", hn.shape)
  
      torch.cuda.synchronize()
      n_iter = 10000
      start = time.time()
      with torch.no_grad():
          rnn.eval()
          for i in range(n_iter):
              rnn.forward(input)
      torch.cuda.synchronize()
      print("Time:", round((time.time() - start), 2), "sec")
  
imports: import time
  import torch
  import torch.nn
  from sru import SRU, SRUCell
  import multiprocessing
label_desc: Use image node_id 1 for calling a global function with example usage: benchmark_gru_gpu() without return types