code: def forward(self, x, noise_level=None):
      if noise_level is None:
          noise_level = torch.randint(
              0, self.max_noise_level, (x.shape[0],), device=x.device
          ).long()
      else:
          assert isinstance(noise_level, torch.Tensor)
      x = self.scale(x)
      z = self.q_sample(x, noise_level)
      z = self.unscale(z)
      noise_level = self.time_embed(noise_level)
      return z, noise_level
  
imports: import torch
  import torch.nn
  import kornia
  from torch.utils.checkpoint import checkpoint
  from transformers import T5Tokenizer, T5EncoderModel, CLIPTokenizer, CLIPTextModel
  import open_clip
  from ldm.util import default, count_params, autocast
  from ldm.modules.diffusionmodules.upscaling import ImageConcatWithNoiseAugmentation
  from ldm.modules.diffusionmodules.openaimodel import Timestep
label_desc: Use image node_id 4 for calling the CLIPEmbeddingNoiseAugmentation obj's underlying member method code with example usage: obj.forward(x, noise_level) and returns: z,  noise_level