code: def forward(self, text_embed, return_loss=True, dry_run=False):
      out = self.model()
      out = norm_siren_output(out)
  
      if not return_loss:
          return out
  
      # determine upper and lower sampling bound
      width = out.shape[-1]
      lower_bound = self.lower_bound_cutout
      if self.saturate_bound:
          progress_fraction = (
              self.num_batches_processed / self.total_batches
          )
          lower_bound += (
              self.saturate_limit - self.lower_bound_cutout
          ) * progress_fraction
  
      # sample cutout sizes between lower and upper bound
      sizes = self.sample_sizes(
          lower_bound, self.upper_bound_cutout, width, self.gauss_mean
      )
  
      # create normalized random cutouts
      if self.do_cutout:
          image_pieces = [
              rand_cutout(
                  out,
                  size,
                  center_bias=self.center_bias,
                  center_focus=self.center_focus,
              )
              for size in sizes
          ]
          image_pieces = [
              interpolate(piece, self.input_resolution)
              for piece in image_pieces
          ]
      else:
          image_pieces = [
              interpolate(out.clone(), self.input_resolution)
              for _ in sizes
          ]
  
      # normalize
      image_pieces = torch.cat(
          [self.normalize_image(piece) for piece in image_pieces]
      )
  
      # calc image embedding
      with autocast(enabled=False):
          image_embed = self.perceptor.encode_image(image_pieces)
  
      # calc loss
      # loss over averaged features of cutouts
      avg_image_embed = image_embed.mean(dim=0).unsqueeze(0)
      averaged_loss = (
          -self.loss_coef
          * torch.cosine_similarity(
              text_embed, avg_image_embed, dim=-1
          ).mean()
      )
      # loss over all cutouts
      general_loss = (
          -self.loss_coef
          * torch.cosine_similarity(
              text_embed, image_embed, dim=-1
          ).mean()
      )
      # merge losses
      loss = averaged_loss * (self.averaging_weight) + general_loss * (
          1 - self.averaging_weight
      )
  
      # count batches
      if not dry_run:
          self.num_batches_processed += self.batch_size
  
      return out, 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 3 for calling the DeepDaze obj's underlying member method code with example usage: obj.forward(text_embed, return_loss, dry_run) and returns: out,  loss, out