code: def log_metric(
      self,
      batch: Dict,
      output: Tuple,
      loss: torch.Tensor,
      step_type: str,
  ):
      logit_m, logit_u, _ = output
      self.log(
          f"{step_type}_loss",
          loss.item(),
          on_step=True,
          on_epoch=True,
      )
      acc_m = _compute_accuracy(logit_m)
      acc_u = _compute_accuracy(logit_u)
      self.log(
          f"{step_type}_acc_m",
          acc_m,
          on_step=True,
          on_epoch=True,
          sync_dist=True,
          prog_bar=step_type == "train",
      )
      self.log(
          f"{step_type}_acc_u",
          acc_u,
          on_step=True,
          on_epoch=True,
          sync_dist=True,
          prog_bar=step_type == "train",
      )
  
imports: import logging
  import pathlib
  from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, RawDescriptionHelpFormatter
  from functools import partial
  from typing import Dict, Tuple
  import torch
  import torchaudio.models
  from lightning.pytorch import seed_everything, Trainer
  from lightning.pytorch.callbacks import ModelCheckpoint
  from .data_modules import HuBERTDataModule
  from .lightning_modules import SSLPretrainModule
  from .losses import hubert_loss
  from .lr_schedulers import LinearDecayLRScheduler
label_desc: Use image node_id 2 for calling the HuBERTModule obj's underlying member method code with example usage: obj.log_metric(batch, output, loss, step_type) without return types