code: def forward(self, x, no_dropout=False):
      # x is assumed to be in range [-1,1]
      out = self.model.encode_image(self.preprocess(x))
      out = out.to(x.dtype)
      if self.ucg_rate > 0.0 and not no_dropout:
          out = (
              torch.bernoulli(
                  (1.0 - self.ucg_rate)
                  * torch.ones(out.shape[0], device=out.device)
              )[:, None]
              * out
          )
      return out
  
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 3 for calling the ClipImageEmbedder obj's underlying member method code with example usage: obj.forward(x, no_dropout) and returns: out