class_docstr: Uses the T5 transformer encoder for text
class_docstr_tok: ["Uses","the","T5","transformer","encoder","for","text"]
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)
  
      z = outputs.last_hidden_state
      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 FrozenT5Embedder obj's underlying member method code with example usage: obj.forward(text) and returns: z