code: def trt7_do_inference_v2(
      args, context, bindings, inputs, outputs, stream
  ):
      # Transfer input data to the GPU.
      [
          cuda.memcpy_htod_async(inp.device, inp.host, stream)
          for inp in inputs
      ]
      # warmup
      for _ in range(args.warmup):
          context.execute_async_v2(
              bindings=bindings, stream_handle=stream.handle
          )
      # Run inference.
      t_start = time.time()
      for _ in range(args.repeats):
          context.execute_async_v2(
              bindings=bindings, stream_handle=stream.handle
          )
      elapsed_time = time.time() - t_start
      latency = elapsed_time / args.repeats * 1000
  
      # Transfer predictions back from the GPU.
      [
          cuda.memcpy_dtoh_async(out.host, out.device, stream)
          for out in outputs
      ]
      # Synchronize the stream
      stream.synchronize()
      # Return only the host outputs.
      return [out.host for out in outputs], latency
  
imports: import argparse
  import os
  import time
  import numpy
  import paddle
  import tensorrt
  import pycuda.driver
  import pycuda.autoinit
  import onnx
  import onnxruntime
  from paddleseg.cvlibs import Config, SegBuilder
  from paddleseg.utils import logger, utils
label_desc: Use image node_id 3 for calling the TRTPredictorV2 obj's underlying member method code with example usage: obj.trt7_do_inference_v2(args, context, bindings, inputs, outputs, stream) and returns: unknown,  latency