code: def apply_model(self, x, t, cond, return_ids=False):
      self.conditioning_key = "crossattn"
      if isinstance(cond, dict):
          # hybrid case, cond is exptected to be a dict
          pass
      else:
          if not isinstance(cond, list):
              cond = [cond]
          # key = 'c_concat' if self.model.conditioning_key == 'concat' else 'c_crossattn'
          key = "c_crossattn"
          cond = {key: cond}
      # UNET TRT
      cc = torch.cat(
          cond["c_crossattn"], 1
      )  # needs to be changed I think
      out = self.unet.infer(
          {
              "x": device_view(x.contiguous()),
              "t": device_view(t.type(torch.int32).contiguous()),
              "context": device_view(cc.contiguous()),
          }
      )["logits"].clone()
      if isinstance(out, tuple) and not return_ids:
          return out[0]
      else:
          return out
  
imports: import logging
  import os
  import tempfile
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from omegaconf import OmegaConf
  from polygraphy import cuda
  from transformers import CLIPTokenizer
  from nemo.collections.multimodal.models.text_to_image.stable_diffusion.ldm.ddpm import LatentDiffusion
  from nemo.collections.multimodal.modules.nerf.guidance.txt2img_guidance_base import Txt2ImgGuidanceBase
  from nemo.collections.multimodal.modules.nerf.utils.trt_engine import Engine, device_view
  from nemo.collections.multimodal.modules.stable_diffusion.diffusionmodules.util import extract_into_tensor, make_beta_schedule
  from nemo.collections.multimodal.parts.stable_diffusion.utils import default
  from nemo.collections.multimodal.parts.utils import randn_like
  from nemo.collections.nlp.parts.nlp_overrides import NLPSaveRestoreConnector
label_desc: Use image node_id 6 for calling the LatentDiffusionWrapper obj's underlying member method code with example usage: obj.apply_model(x, t, cond, return_ids) and returns: out, out