code: def forward(self, input_sentence: str, *args, **kwargs) -> Tensor:
      # add start and eos tokens to input sentence
      input_sentence = (
          "<|startoftext|> " + input_sentence + " <|endoftext|>"
      )
      # tokenizer returns indices as a string
      tokenized_sentence = self.tokenizer(input_sentence)
      # convert string to int and then create a tensor
      tokenized_sentence = torch.tensor(
          [int(cap) for cap in tokenized_sentence], dtype=torch.long
      )
      return tokenized_sentence
  
file_path: ml-cvnets/data/text_tokenizer/clip_tokenizer.py
imports: import argparse
  import torch
  from torch import Tensor
  from torchtext.transforms import CLIPTokenizer
  from data.text_tokenizer import TOKENIZER_REGISTRY, BaseTokenizer
  from utils import logger
  from utils.download_utils import get_local_path
label_desc: Use image node_id 7 for calling the ClipTokenizer obj's underlying member method code with example usage: obj.forward(input_sentence) and returns: tokenized_sentence