class_docstr: Hierarchical Attention Network (RNN baseline model)
docstr: Initialise variables to store metrics
code: def init_metrics(self):
      """Initialise variables to store metrics"""
      ### TRAIN
      self.train_accuracy = torchmetrics.Accuracy(
          threshold=0.5,
          num_classes=self.hparams.cls_num_targets,
          average="macro",
      )
      self.train_precision = torchmetrics.Precision(
          threshold=0.5,
          num_classes=self.hparams.cls_num_targets,
          average="macro",
      )
      self.train_recall = torchmetrics.Recall(
          threshold=0.5,
          num_classes=self.hparams.cls_num_targets,
          average="macro",
      )
      self.train_f1 = torchmetrics.F1Score(
          threshold=0.5,
          num_classes=self.hparams.cls_num_targets,
          average="macro",
      )
  
      ##### VALIDATION
      self.val_cr_bacc = CorrectedBAcc(
          alpha=self.hparams.asym_alpha,
          beta=self.hparams.asym_beta,
          threshold=0.5,
          average="micro",
      )
      self.val_cr_f1 = CorrectedF1(
          alpha=self.hparams.asym_alpha,
          beta=self.hparams.asym_beta,
          threshold=0.5,
          average="micro",
      )
      self.val_cr_mcc = CorrectedMCC(
          alpha=self.hparams.asym_alpha,
          beta=self.hparams.asym_beta,
          threshold=0.5,
          average="micro",
      )
      self.val_aul = AUL()
  
      ##### TEST
      self.test_cr_bacc = CorrectedBAcc(
          alpha=self.hparams.asym_alpha,
          beta=self.hparams.asym_beta,
          threshold=0.5,
          average="micro",
      )
      self.test_cr_f1 = CorrectedF1(
          alpha=self.hparams.asym_alpha,
          beta=self.hparams.asym_beta,
          threshold=0.5,
          average="micro",
      )
      self.test_cr_mcc = CorrectedMCC(
          alpha=self.hparams.asym_alpha,
          beta=self.hparams.asym_beta,
          threshold=0.5,
          average="micro",
      )
      self.test_aul = AUL()
  
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 4 for calling the FFN obj's underlying member method code with example usage: obj.init_metrics() without return types