code: def get_x_noisy(self, x, t, noise=None):
      noise = default(noise, lambda: torch.randn_like(x))
      continuous_sqrt_alpha_cumprod = None
      if self.diffusion_model.use_continuous_noise:
          continuous_sqrt_alpha_cumprod = (
              self.diffusion_model.sample_continuous_noise_level(
                  x.shape[0], t + 1
              )
          )
          # todo: make sure t+1 is correct here
  
      return self.diffusion_model.q_sample(
          x_start=x,
          t=t,
          noise=noise,
          continuous_sqrt_alpha_cumprod=continuous_sqrt_alpha_cumprod,
      )
  
imports: import os
  import torch
  import pytorch_lightning
  from omegaconf import OmegaConf
  from torch.nn import functional
  from torch.optim import AdamW
  from torch.optim.lr_scheduler import LambdaLR
  from copy import deepcopy
  from einops import rearrange
  from glob import glob
  from natsort import natsorted
  from ldm.modules.diffusionmodules.openaimodel import EncoderUNetModel, UNetModel
  from ldm.util import log_txt_as_img, default, ismap, instantiate_from_config
label_desc: Use image node_id 5 for calling the NoisyLatentImageClassifier obj's underlying member method code with example usage: obj.get_x_noisy(x, t, noise) and returns: self