code: def trt7_run(args, onnx_file_path, input_data):
      engine_file_path = onnx_file_path[0:-5] + ".trt"
      input_shape = input_data.shape
      with TRTPredictorV2.trt7_get_engine(
          onnx_file_path, input_shape
      ) as engine, engine.create_execution_context() as context:
          (
              inputs,
              outputs,
              bindings,
              stream,
          ) = TRTPredictorV2.allocate_buffers(engine)
          if args.enable_profile:
              context.profiler = trt.Profiler()
  
          # Do inference
          # Set host input to the image. The common.do_inference function will copy the input to the GPU before executing.
          inputs[0].host = input_data
          trt_outputs, latency = TRTPredictorV2.trt7_do_inference_v2(
              args,
              context,
              bindings=bindings,
              inputs=inputs,
              outputs=outputs,
              stream=stream,
          )
          return trt_outputs[0], 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 5 for calling the TRTPredictorV2 obj's underlying member method code with example usage: obj.trt7_run(args, onnx_file_path, input_data) and returns: trt_outputs,  latency