code: def hf_xglm(model, inputs, tokenizer):
      inputs = inputs.to("cuda:0")
      torch.cuda.synchronize()
      start_time = time.perf_counter()
      generated_ids = model.generate(inputs, max_length=100, top_k=1)
      torch.cuda.synchronize()
      end_time = time.perf_counter()
      return generated_ids, end_time - start_time
  
file_path: lightseq/examples/inference/python/test/ls_xglm.py
imports: import time
  import argparse
  import torch
  import numpy
  import lightseq.inference
  from transformers import AutoTokenizer, AutoModelForCausalLM
label_desc: Use image node_id 2 for calling a global function with example usage: hf_xglm(model, inputs, tokenizer) and returns: generated_ids,  unknown