code: def save(self, filename):
      state_dict = {}
      optimizer_saved_dict = {}
  
      for k, v in self.layers.items():
          state_dict[k] = (v[0].state_dict(), v[1].state_dict())
  
      state_dict["step"] = self.step
      state_dict["name"] = self.name
      state_dict["layer_structure"] = self.layer_structure
      state_dict["activation_func"] = self.activation_func
      state_dict["is_layer_norm"] = self.add_layer_norm
      state_dict["weight_initialization"] = self.weight_init
      state_dict["sd_checkpoint"] = self.sd_checkpoint
      state_dict["sd_checkpoint_name"] = self.sd_checkpoint_name
      state_dict["activate_output"] = self.activate_output
      state_dict["use_dropout"] = self.use_dropout
      state_dict["dropout_structure"] = self.dropout_structure
      state_dict["last_layer_dropout"] = (
          (self.dropout_structure[-2] != 0)
          if self.dropout_structure is not None
          else self.last_layer_dropout
      )
      state_dict["optional_info"] = (
          self.optional_info if self.optional_info else None
      )
  
      if self.optimizer_name is not None:
          optimizer_saved_dict["optimizer_name"] = self.optimizer_name
  
      torch.save(state_dict, filename)
      if shared.opts.save_optimizer_state and self.optimizer_state_dict:
          optimizer_saved_dict["hash"] = self.shorthash()
          optimizer_saved_dict[
              "optimizer_state_dict"
          ] = self.optimizer_state_dict
          torch.save(optimizer_saved_dict, filename + ".optim")
  
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 7 for calling the Hypernetwork obj's underlying member method code with example usage: obj.save(filename) without return types