code: def create_text_path(
      context_length, text=None, img=None, encoding=None, separator=None
  ):
      if text is not None:
          if separator is not None and separator in text:
              # Reduces filename to first epoch text
              text = text[
                  : text.index(
                      separator,
                  )
              ]
          input_name = text.replace(" ", "_")[:context_length]
      elif img is not None:
          if isinstance(img, str):
              input_name = "".join(
                  img.replace(" ", "_").split(".")[:-1]
              )
          else:
              input_name = "PIL_img"
      else:
          input_name = "your_encoding"
      return input_name
  
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 8 for calling a global function with example usage: create_text_path(context_length, text, img, encoding, separator) and returns: input_name