class_docstr: Uses the CLIP transformer encoder for text (from huggingface)
class_docstr_tok: ["Uses","the","CLIP","transformer","encoder","for","text","(","from","huggingface",")"]
code: def forward(self, text):
      batch_encoding = self.tokenizer(
          text,
          truncation=True,
          max_length=self.max_length,
          return_length=True,
          return_overflowing_tokens=False,
          padding="max_length",
          return_tensors="pt",
      )
      tokens = batch_encoding["input_ids"].to(self.device)
      outputs = self.transformer(
          input_ids=tokens, output_hidden_states=self.layer == "hidden"
      )
      if self.layer == "last":
          z = outputs.last_hidden_state
      elif self.layer == "pooled":
          z = outputs.pooler_output[:, None, :]
      else:
          z = outputs.hidden_states[self.layer_idx]
      return z
  
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 FrozenCLIPEmbedder obj's underlying member method code with example usage: obj.forward(text) and returns: z