code: def decode_latents(self, latents):
      warnings.warn(
          "The decode_latents method is deprecated and will be removed in a future version. Please"
          " use VaeImageProcessor instead",
          FutureWarning,
      )
      latents = 1 / self.vae.config.scaling_factor * latents
      image = self.vae.decode(latents, return_dict=False)[0]
      image = (image / 2 + 0.5).clamp(0, 1)
      # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
      image = image.cpu().permute(0, 2, 3, 1).float().numpy()
      return image
  
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 3 for calling the TensorRTStableDiffusionControlNetImg2ImgPipeline obj's underlying member method code with example usage: obj.decode_latents(latents) and returns: image