code: def _compute_accuracy(logits: torch.Tensor):
      with torch.no_grad():
          max = logits.argmax(-1) == 0
          min = logits.argmin(-1) == 0
          both = max & min
          corr = max.long().sum().item() - both.long().sum().item()
          count = max.numel()
      return corr / count
  
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 1 for calling a global function with example usage: _compute_accuracy(logits) and returns: unknown