code: def tokenize(
      self, texts: Union[str, List[str]], context_length: int = 77
  ) -> torch.LongTensor:
      if isinstance(texts, str):
          texts = [texts]
  
      sot_token = self.tokenizer.encoder["<|startoftext|>"]
      eot_token = self.tokenizer.encoder["<|endoftext|>"]
      all_tokens = [
          [sot_token] + self.tokenizer.encode(text) + [eot_token]
          for text in texts
      ]
      result = torch.zeros(
          len(all_tokens), context_length, dtype=torch.long
      )
  
      for i, tokens in enumerate(all_tokens):
          if len(tokens) > context_length:
              st = torch.randint(
                  len(tokens) - context_length + 1, (1,)
              )[0].item()
              tokens = tokens[st : st + context_length]
          result[i, : len(tokens)] = torch.tensor(tokens)
  
      return result
  
imports: import copy
  from typing import List, Union
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from mmengine.logging import print_log
  from torch import Tensor
  from mmdet.datasets import LVISV1Dataset
  from mmdet.models.detectors.cascade_rcnn import CascadeRCNN
  from mmdet.registry import MODELS
  from mmdet.structures import SampleList
label_desc: Use image node_id 4 for calling the CLIPTextEncoder obj's underlying member method code with example usage: obj.tokenize(texts, context_length) and returns: result