class_docstr: Uses the T5 transformer encoder for text
class_docstr_tok: ["Uses","the","T5","transformer","encoder","for","text"]
code: def __init__(
      self,
      version="google/t5-v1_1-large",
      device="cuda",
      max_length=77,
      freeze=True,
  ):  # others are google/t5-v1_1-xl and google/t5-v1_1-xxl
      super().__init__()
      self.tokenizer = T5Tokenizer.from_pretrained(version)
      self.transformer = T5EncoderModel.from_pretrained(version)
      self.device = device
      self.max_length = max_length  # TODO: typical value?
      if freeze:
          self.freeze()
  
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 1 to create a new FrozenT5Embedder object from inherited base classes: AbstractEncoder with example: obj = FrozenT5Embedder(version, device, max_length, freeze)