class_docstr: Hierarchical Attention Network (RNN baseline model)
code: def init_encoder(self):
      if self.hparams.encoder_type == "neural":
          log.info("Encoder is a NEURAL NETWORK")
          self.encoder = NNEncoder(self.hparams)
      elif self.hparams.encoder_type == "logistic":
          log.info("Encoder is a LOGISTIC REGRESSOR")
          self.encoder = LogisticRegression(self.hparams)
      elif self.hparams.encoder_type == "table":
          self.encoder = LifeTable(self.hparams)
      else:
          raise NotImplementedError()
  
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 3 for calling the FFN obj's underlying member method code with example usage: obj.init_encoder() without return types