class_docstr: Uses a pretrained BERT tokenizer by huggingface. Vocab size: 30522 (?)
class_docstr_tok: ["Uses","a","pretrained","BERT","tokenizer","by","huggingface",".","Vocab","size",":","30522","(","?",")"]
code: def __init__(self, device="cuda", vq_interface=True, max_length=77):
      super().__init__()
      from transformers import (
          BertTokenizerFast,
      )  # TODO: add to reuquirements
  
      self.tokenizer = BertTokenizerFast.from_pretrained(
          "bert-base-uncased"
      )
      self.device = device
      self.vq_interface = vq_interface
      self.max_length = max_length
  
imports: import torch
  import torch.nn
  from functools import partial
  import clip
  from einops import rearrange, repeat
  from transformers import CLIPTokenizer, CLIPTextModel
  import kornia
  from ldm.modules.x_transformer import Encoder, TransformerWrapper
label_desc: Use image node_id 1 to create a new BERTTokenizer object from inherited base classes: AbstractEncoder with example: obj = BERTTokenizer(device, vq_interface, max_length)