code: def train_step(self, epoch, iteration):
      total_loss = 0
  
      for _ in range(self.gradient_accumulate_every):
          with autocast(enabled=True):
              out, loss = self.model(self.clip_encoding)
          loss = loss / self.gradient_accumulate_every
          total_loss += loss
          self.scaler.scale(loss).backward()
      out = out.cpu().float().clamp(0.0, 1.0)
      self.scaler.step(self.optimizer)
      self.scaler.update()
      self.optimizer.zero_grad()
  
      if (iteration % self.save_every == 0) and self.save_progress:
          self.save_image(epoch, iteration, img=out)
  
      return out, total_loss
  
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 9 for calling the Imagine obj's underlying member method code with example usage: obj.train_step(epoch, iteration) and returns: out,  total_loss