code: def trt_inference(trt_path: str, image: np.ndarray):
      # Load the network in Inference Engine
      trt_logger = trt.Logger(trt.Logger.WARNING)
      with open(trt_path, "rb") as f, trt.Runtime(
          trt_logger
      ) as runtime:
          engine = runtime.deserialize_cuda_engine(f.read())
  
      with engine.create_execution_context() as context:
          # Set input shape based on image dimensions for inference
          context.set_binding_shape(
              engine.get_binding_index("input"),
              (1, 3, image.shape[-2], image.shape[-1]),
          )
          # Allocate host and device buffers
          bindings = []
          for binding in engine:
              binding_idx = engine.get_binding_index(binding)
              size = trt.volume(context.get_binding_shape(binding_idx))
              dtype = trt.nptype(engine.get_binding_dtype(binding))
              if engine.binding_is_input(binding):
                  input_buffer = np.ascontiguousarray(image)
                  input_memory = cuda.mem_alloc(image.nbytes)
                  bindings.append(int(input_memory))
              else:
                  output_buffer = cuda.pagelocked_empty(size, dtype)
                  output_memory = cuda.mem_alloc(output_buffer.nbytes)
                  bindings.append(int(output_memory))
  
          stream = cuda.Stream()
          # Transfer input data to the GPU.
          cuda.memcpy_htod_async(input_memory, input_buffer, stream)
          # Run inference
          context.execute_async_v2(
              bindings=bindings, stream_handle=stream.handle
          )
          # Transfer prediction output from the GPU.
          cuda.memcpy_dtoh_async(output_buffer, output_memory, stream)
          # Synchronize the stream
          stream.synchronize()
  
          res_trt = np.reshape(output_buffer, (1, -1))
  
      return res_trt
  
file_path: deep-learning-for-image-processing/deploying_service/deploying_pytorch/convert_tensorrt/convert_resnet34/compare_onnx_and_trt.py
imports: import numpy
  import tensorrt
  import onnxruntime
  import pycuda.driver
  import pycuda.autoinit
label_desc: Use image node_id 3 for calling a global function with example usage: trt_inference(trt_path, image) and returns: res_trt