code: def run(args):
      torch.manual_seed(1)
  
      batch_size = 3
      input_size = 5
      hidden_size = 7
      seq_len = 4
      num_layers = 2
  
      sru_kwargs = {
          "input_size": input_size,
          "hidden_size": hidden_size,
          "num_layers": num_layers,
          "bidirectional": True,
          "dropout": 0.1,
          "rescale": False,
      }
  
      inputs = torch.rand(seq_len, batch_size, input_size)
      model = sru.SRU(**sru_kwargs).eval()
  
      with torch.no_grad():
          outputs = model(inputs)
  
      artifact_dict = {
          "outputs": outputs,
          "inputs": inputs,
          "model_state": model.state_dict(),
          "sru_kwargs": sru_kwargs,
          "sru.__version__": sru.__version__,
      }
      torch.save(artifact_dict, args.out_artifact)
  
imports: import torch
  import sru
  import argparse
label_desc: Use image node_id 1 for calling a global function with example usage: run(args) without return types