code: def update_story_encoding(self, epoch, iteration):
      if self.separator is not None:
          self.words = " ".join(
              self.all_words[: self.index_of_first_separator()]
          )
          # removes separator from epoch-text
          self.words = self.words.replace(self.separator, "")
          self.all_words = self.all_words[
              self.index_of_first_separator() :
          ]
      else:
          if self.words is None:
              self.words = " ".join(
                  self.all_words[: self.num_start_words]
              )
              self.all_words = self.all_words[self.num_start_words :]
          else:
              # add words_per_epoch new words
              count = 0
              while (
                  count < self.words_per_epoch
                  and len(self.all_words) > 0
              ):
                  new_word = self.all_words[0]
                  self.words = " ".join(
                      self.words.split(" ") + [new_word]
                  )
                  self.all_words = self.all_words[1:]
                  count += 1
              # remove words until it fits in context length
              while len(self.words) > self.perceptor.context_length:
                  # remove first word
                  self.words = " ".join(self.words.split(" ")[1:])
      # get new encoding
      print("Now thinking of: ", '"', self.words, '"')
      sequence_number = self.get_img_sequence_number(epoch, iteration)
      # save new words to disc
      with open("story_transitions.txt", "a") as f:
          f.write(f"{epoch}, {sequence_number}, {self.words}\n")
  
      encoding = self.create_text_encoding(self.words)
      return encoding
  
imports: import os
  import subprocess
  import sys
  import random
  from datetime import datetime
  from pathlib import Path
  import torch
  import torch.nn.functional
  from siren_pytorch import SirenNet, SirenWrapper
  from torch import nn
  from torch.cuda.amp import GradScaler, autocast
  from torch_optimizer import DiffGrad, AdamP
  import numpy
  from PIL import Image
  from imageio import imread, mimsave
  import torchvision.transforms
  from tqdm import trange, tqdm
  from .clip import load, tokenize
label_desc: Use image node_id 7 for calling the Imagine obj's underlying member method code with example usage: obj.update_story_encoding(epoch, iteration) and returns: encoding