code: def __call__(self, **kwargs):
      context = self.context
      stream = self.stream
      bindings = self.bindings
  
      host_inputs = self.host_inputs
      cuda_inputs = self.cuda_inputs
      host_outputs = self.host_outputs
      cuda_outputs = self.cuda_outputs
  
      for idx, input_name in enumerate(self.input_names):
          _input = kwargs[input_name]
          np.copyto(host_inputs[idx], _input)
          # transfer input data to the GPU
          cuda.memcpy_htod_async(
              cuda_inputs[idx], host_inputs[idx], stream
          )
  
      context.execute_async_v2(
          bindings=bindings, stream_handle=stream.handle
      )
  
      result = {}
      for idx, output_name in enumerate(self.output_names):
          # transfer predictions back from the GPU
          cuda.memcpy_dtoh_async(
              host_outputs[idx], cuda_outputs[idx], stream
          )
          result[output_name] = host_outputs[idx]
  
      stream.synchronize()
  
      return result
  
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 2 for calling the TensorRTModel obj's underlying member method code with example usage: obj.__call__() and returns: result