code: def __init__(
      self,
      trt_engine_path,
      **kwargs,
  ):
      cuda.init()
      stream = cuda.Stream()
      TRT_LOGGER = trt.Logger(trt.Logger.VERBOSE)
      trt.init_libnvinfer_plugins(TRT_LOGGER, "")
      trt_runtime = trt.Runtime(TRT_LOGGER)
      engine = load_engine(trt_runtime, trt_engine_path)
      context = engine.create_execution_context()
  
      # allocates memory for network inputs/outputs on both CPU and GPU
      host_inputs = []
      cuda_inputs = []
      host_outputs = []
      cuda_outputs = []
      bindings = []
      input_names = []
      output_names = []
  
      for binding in engine:
          datatype = engine.get_binding_dtype(binding)
          if datatype == trt.DataType.HALF:
              dtype = np.float16
          else:
              dtype = np.float32
  
          shape = tuple(engine.get_binding_shape(binding))
          host_mem = cuda.pagelocked_empty(shape, dtype)
          cuda_mem = cuda.mem_alloc(host_mem.nbytes)
          bindings.append(int(cuda_mem))
  
          if engine.binding_is_input(binding):
              host_inputs.append(host_mem)
              cuda_inputs.append(cuda_mem)
              input_names.append(binding)
          else:
              host_outputs.append(host_mem)
              cuda_outputs.append(cuda_mem)
              output_names.append(binding)
  
      self.stream = stream
      self.context = context
      self.engine = engine
  
      self.host_inputs = host_inputs
      self.cuda_inputs = cuda_inputs
      self.host_outputs = host_outputs
      self.cuda_outputs = cuda_outputs
      self.bindings = bindings
      self.batch_size = engine.max_batch_size
  
      self.input_names = input_names
      self.output_names = output_names
  
imports: import argparse
  import atexit
  import inspect
  import os
  import time
  import warnings
  from typing import Any, Callable, Dict, List, Optional, Union
  import numpy
  import PIL.Image
  import pycuda.driver
  import tensorrt
  import torch
  from PIL import Image
  from pycuda.tools import make_default_context
  from transformers import CLIPTokenizer
  from diffusers import OnnxRuntimeModel, StableDiffusionImg2ImgPipeline, UniPCMultistepScheduler
  from diffusers.image_processor import VaeImageProcessor
  from diffusers.pipelines.pipeline_utils import DiffusionPipeline
  from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
  from diffusers.schedulers import KarrasDiffusionSchedulers
  from diffusers.utils import deprecate, logging, replace_example_docstring
  from diffusers.utils.torch_utils import randn_tensor
label_desc: Use image node_id 1 to create a new TensorRTModel object  with example: obj = TensorRTModel(trt_engine_path)