code: def parse_dropout_structure(
      layer_structure, use_dropout, last_layer_dropout
  ):
      if layer_structure is None:
          layer_structure = [1, 2, 1]
      if not use_dropout:
          return [0] * len(layer_structure)
      dropout_values = [0]
      dropout_values.extend([0.3] * (len(layer_structure) - 3))
      if last_layer_dropout:
          dropout_values.append(0.3)
      else:
          dropout_values.append(0)
      dropout_values.append(0)
      return dropout_values
  
imports: import datetime
  import glob
  import html
  import os
  import inspect
  from contextlib import closing
  import modules.textual_inversion.dataset
  import torch
  import tqdm
  from einops import rearrange, repeat
  from ldm.util import default
  from modules import devices, sd_models, shared, sd_samplers, hashes, sd_hijack_checkpoint, errors
  from modules.textual_inversion import textual_inversion, logging
  from modules.textual_inversion.learn_schedule import LearnRateScheduler
  from torch import einsum
  from torch.nn.init import normal_, xavier_normal_, xavier_uniform_, kaiming_normal_, kaiming_uniform_, zeros_
  from collections import deque
  from statistics import stdev, mean
label_desc: Use image node_id 1 for calling a global function with example usage: parse_dropout_structure(layer_structure, use_dropout, last_layer_dropout) and returns: dropout_values, unknown