code: def __init__(self, hparams) -> None:
      super().__init__()
      self.ff1 = nn.Sequential(
          nn.Linear(hparams.input_size, hparams.hidden_size),
          Swish(),
          ScaleNorm(hidden_size=hparams.hidden_size),
          nn.AlphaDropout(p=hparams.dropout),
      )
      self.ff2 = nn.Sequential(
          nn.Linear(hparams.hidden_size, hparams.hidden_size * 4),
          Swish(),
          ScaleNorm(hidden_size=hparams.hidden_size * 4),
          nn.AlphaDropout(p=hparams.dropout),
      )
      self.ff3 = nn.Sequential(
          nn.Linear(hparams.hidden_size * 4, hparams.hidden_size * 2),
          Swish(),
          ScaleNorm(hidden_size=hparams.hidden_size * 2),
          nn.AlphaDropout(p=hparams.dropout / 3),
      )
  
      module = [
          nn.Sequential(
              nn.Linear(hparams.input_size, hparams.hidden_size),
              Swish(),
              ScaleNorm(hidden_size=hparams.hidden_size),
              nn.AlphaDropout(p=hparams.dropout),
          )
      ]
      for i in range(hparams.n_layers - 1):
          module.append(
              nn.Sequential(
                  nn.Linear(hparams.hidden_size, hparams.hidden_size),
                  Swish(),
                  ScaleNorm(hidden_size=hparams.hidden_size),
                  nn.AlphaDropout(p=hparams.dropout),
              )
          )
  
      print("NUM Modules:", len(module))
  
      self.ff = nn.ModuleList(module)
  
      self.out = nn.Linear(hparams.hidden_size, hparams.cls_num_targets)
  
imports: from turtle import hideturtle
  import pytorch_lightning
  import torch
  import torch.nn.functional
  import torch.nn
  from pytorch_lightning import seed_everything
  from pathlib import Path
  from src.transformer.transformer_utils import *
  from src.transformer.metrics import CorrectedBAcc, CorrectedF1, CorrectedMCC, AUL
  import torchmetrics
label_desc: Use image node_id 1 to create a new NNEncoder object from inherited base classes: nn with example: obj = NNEncoder(hparams)