code: def forward(self):
      if exists(self.start_image):
          tqdm.write("Preparing with initial image...")
          optim = DiffGrad(
              self.model.model.parameters(), lr=self.start_image_lr
          )
          pbar = trange(self.start_image_train_iters, desc="iteration")
          try:
              for _ in pbar:
                  loss = self.model.model(self.start_image)
                  loss.backward()
                  pbar.set_description(f"loss: {loss.item():.2f}")
  
                  optim.step()
                  optim.zero_grad()
          except KeyboardInterrupt:
              print("interrupted by keyboard, gracefully exiting")
              return exit()
  
          del self.start_image
          del optim
  
      tqdm.write(
          f'Imagining "{self.textpath}" from the depths of my weights...'
      )
  
      with torch.no_grad():
          self.model(
              self.clip_encoding, dry_run=True
          )  # do one warmup step due to potential issue with CLIP and CUDA
  
      if self.open_folder:
          open_folder("./")
          self.open_folder = False
  
      try:
          for epoch in trange(self.epochs, desc="epochs"):
              pbar = trange(self.iterations, desc="iteration")
              for i in pbar:
                  _, loss = self.train_step(epoch, i)
                  pbar.set_description(f"loss: {loss.item():.2f}")
  
              # Update clip_encoding per epoch if we are creating a story
              if self.create_story:
                  self.clip_encoding = self.update_story_encoding(
                      epoch, i
                  )
      except KeyboardInterrupt:
          print("interrupted by keyboard, gracefully exiting")
          return
  
      self.save_image(epoch, i)  # one final save at end
  
      if (self.save_gif or self.save_video) and self.save_progress:
          self.generate_gif()
  
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 13 for calling the Imagine obj's underlying member method code with example usage: obj.forward() and returns: exit