code: def __init__(
      self,
      dim,
      state_dict=None,
      layer_structure=None,
      activation_func=None,
      weight_init="Normal",
      add_layer_norm=False,
      activate_output=False,
      dropout_structure=None,
  ):
      super().__init__()
  
      self.multiplier = 1.0
  
      assert (
          layer_structure is not None
      ), "layer_structure must not be None"
      assert (
          layer_structure[0] == 1
      ), "Multiplier Sequence should start with size 1!"
      assert (
          layer_structure[-1] == 1
      ), "Multiplier Sequence should end with size 1!"
  
      linears = []
      for i in range(len(layer_structure) - 1):
          # Add a fully-connected layer
          linears.append(
              torch.nn.Linear(
                  int(dim * layer_structure[i]),
                  int(dim * layer_structure[i + 1]),
              )
          )
  
          # Add an activation func except last layer
          if (
              activation_func == "linear"
              or activation_func is None
              or (i >= len(layer_structure) - 2 and not activate_output)
          ):
              pass
          elif activation_func in self.activation_dict:
              linears.append(self.activation_dict[activation_func]())
          else:
              raise RuntimeError(
                  f"hypernetwork uses an unsupported activation function: {activation_func}"
              )
  
          # Add layer normalization
          if add_layer_norm:
              linears.append(
                  torch.nn.LayerNorm(int(dim * layer_structure[i + 1]))
              )
  
          # Everything should be now parsed into dropout structure, and applied here.
          # Since we only have dropouts after layers, dropout structure should start with 0 and end with 0.
          if (
              dropout_structure is not None
              and dropout_structure[i + 1] > 0
          ):
              assert (
                  0 < dropout_structure[i + 1] < 1
              ), "Dropout probability should be 0 or float between 0 and 1!"
              linears.append(
                  torch.nn.Dropout(p=dropout_structure[i + 1])
              )
          # Code explanation : [1, 2, 1] -> dropout is missing when last_layer_dropout is false. [1, 2, 2, 1] -> [0, 0.3, 0, 0], when its True, [0, 0.3, 0.3, 0].
  
      self.linear = torch.nn.Sequential(*linears)
  
      if state_dict is not None:
          self.fix_old_state_dict(state_dict)
          self.load_state_dict(state_dict)
      else:
          for layer in self.linear:
              if (
                  type(layer) == torch.nn.Linear
                  or type(layer) == torch.nn.LayerNorm
              ):
                  w, b = layer.weight.data, layer.bias.data
                  if (
                      weight_init == "Normal"
                      or type(layer) == torch.nn.LayerNorm
                  ):
                      normal_(w, mean=0.0, std=0.01)
                      normal_(b, mean=0.0, std=0)
                  elif weight_init == "XavierUniform":
                      xavier_uniform_(w)
                      zeros_(b)
                  elif weight_init == "XavierNormal":
                      xavier_normal_(w)
                      zeros_(b)
                  elif weight_init == "KaimingUniform":
                      kaiming_uniform_(
                          w,
                          nonlinearity="leaky_relu"
                          if "leakyrelu" == activation_func
                          else "relu",
                      )
                      zeros_(b)
                  elif weight_init == "KaimingNormal":
                      kaiming_normal_(
                          w,
                          nonlinearity="leaky_relu"
                          if "leakyrelu" == activation_func
                          else "relu",
                      )
                      zeros_(b)
                  else:
                      raise KeyError(
                          f"Key {weight_init} is not defined as initialization!"
                      )
      self.to(devices.device)
  
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 to create a new HypernetworkModule object from inherited base classes: torch.nn with example: obj = HypernetworkModule(dim, state_dict, layer_structure, activation_func, weight_init, add_layer_norm, activate_output, dropout_structure)