code: def trainables(self):
      layer_structure = []
      for layer in self.linear:
          if (
              type(layer) == torch.nn.Linear
              or type(layer) == torch.nn.LayerNorm
          ):
              layer_structure += [layer.weight, layer.bias]
      return layer_structure
  
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 4 for calling the HypernetworkModule obj's underlying member method code with example usage: obj.trainables() and returns: layer_structure