code: def load_and_transform_text(text, device):
      if text is None:
          return None
      tokenizer = SimpleTokenizer(bpe_path=BPE_PATH)
      tokens = [tokenizer(t).unsqueeze(0).to(device) for t in text]
      tokens = torch.cat(tokens, dim=0)
      return tokens
  
imports: import logging
  import math
  import torch
  import torch.nn
  import torchaudio
  from PIL import Image
  from pytorchvideo import transforms
  from pytorchvideo.data.clip_sampling import ConstantClipsPerVideoSampler
  from pytorchvideo.data.encoded_video import EncodedVideo
  from torchvision import transforms
  from torchvision.transforms._transforms_video import NormalizeVideo
  from imagebind.models.multimodal_preprocessors import SimpleTokenizer
label_desc: Use image node_id 4 for calling a global function with example usage: load_and_transform_text(text, device) and returns: tokens, None