code: def model_infer(
      inputs,
      context,
      d_inputs,
      h_output0,
      h_output1,
      d_output0,
      d_output1,
      stream,
  ):
      input_ids = np.asarray(inputs["input_ids"], dtype=np.int32)
      attention_mask = np.asarray(
          inputs["attention_mask"], dtype=np.int32
      )
      token_type_ids = np.asarray(
          inputs["token_type_ids"], dtype=np.int32
      )
  
      # Copy inputs
      cuda.memcpy_htod_async(d_inputs[0], input_ids.ravel(), stream)
      cuda.memcpy_htod_async(
          d_inputs[1], attention_mask.ravel(), stream
      )
      cuda.memcpy_htod_async(
          d_inputs[2], token_type_ids.ravel(), stream
      )
      # start time
      start_time = time.time()
      # Run inference
      context.execute_async(
          bindings=[int(d_inp) for d_inp in d_inputs]
          + [int(d_output0), int(d_output1)],
          stream_handle=stream.handle,
      )
      # Transfer predictions back from GPU
      cuda.memcpy_dtoh_async(h_output0, d_output0, stream)
      cuda.memcpy_dtoh_async(h_output1, d_output1, stream)
      # Synchronize the stream and take time
      stream.synchronize()
      # end time
      end_time = time.time()
      infer_time = end_time - start_time
      outputs = (h_output0, h_output1)
      # print(outputs)
      return outputs, infer_time
  
imports: import argparse
  import logging
  import os
  import time
  import timeit
  import datasets
  import numpy
  import torch
  from absl import logging
  from datasets import load_dataset, load_metric
  from torch.utils.data import DataLoader
  import pycuda.autoinit
  import pycuda.driver
  import tensorrt
  import transformers
  from accelerate import Accelerator
  from transformers import AutoTokenizer, EvalPrediction, default_data_collator, set_seed
  from transformers.trainer_pt_utils import nested_concat, nested_truncate
  from utils_qa import postprocess_qa_predictions
label_desc: Use image node_id 1 for calling a global function with example usage: model_infer(inputs, context, d_inputs, h_output0, h_output1, d_output0, d_output1, stream) and returns: outputs,  infer_time