code: def __init__(
      self,
      clip_perceptor,
      clip_norm,
      input_res,
      total_batches,
      batch_size,
      num_layers=8,
      image_width=512,
      loss_coef=100,
      theta_initial=None,
      theta_hidden=None,
      lower_bound_cutout=0.1,  # should be smaller than 0.8
      upper_bound_cutout=1.0,
      saturate_bound=False,
      gauss_sampling=False,
      gauss_mean=0.6,
      gauss_std=0.2,
      do_cutout=True,
      center_bias=False,
      center_focus=2,
      hidden_size=256,
      averaging_weight=0.3,
  ):
      super().__init__()
      # load clip
      self.perceptor = clip_perceptor
      self.input_resolution = input_res
      self.normalize_image = clip_norm
  
      self.loss_coef = loss_coef
      self.image_width = image_width
  
      self.batch_size = batch_size
      self.total_batches = total_batches
      self.num_batches_processed = 0
  
      w0 = default(theta_hidden, 30.0)
      w0_initial = default(theta_initial, 30.0)
  
      siren = SirenNet(
          dim_in=2,
          dim_hidden=hidden_size,
          num_layers=num_layers,
          dim_out=3,
          use_bias=True,
          w0=w0,
          w0_initial=w0_initial,
      )
  
      self.model = SirenWrapper(
          siren, image_width=image_width, image_height=image_width
      )
  
      self.saturate_bound = saturate_bound
      self.saturate_limit = (
          0.75  # cutouts above this value lead to destabilization
      )
      self.lower_bound_cutout = lower_bound_cutout
      self.upper_bound_cutout = upper_bound_cutout
      self.gauss_sampling = gauss_sampling
      self.gauss_mean = gauss_mean
      self.gauss_std = gauss_std
      self.do_cutout = do_cutout
      self.center_bias = center_bias
      self.center_focus = center_focus
      self.averaging_weight = averaging_weight
  
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 1 to create a new DeepDaze object from inherited base classes: nn with example: obj = DeepDaze(clip_perceptor, clip_norm, input_res, total_batches, batch_size, num_layers, image_width, loss_coef, theta_initial, theta_hidden, lower_bound_cutout, upper_bound_cutout, saturate_bound, gauss_sampling, gauss_mean, gauss_std, do_cutout, center_bias, center_focus, hidden_size, averaging_weight)