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 math
  from typing import Optional, Tuple
  import torch
  import torch.nn.functional
  import torchaudio
  import torchaudio.models.wav2vec2.components
  from dataset import _get_lengths_librilightlimited, _get_lengths_librispeech, BucketizeBatchSampler, CollateFnHubert, CollateFnLibriLightLimited, DistributedBatchSampler, HuBERTDataSet
  from lightning.pytorch import LightningModule
  from loss import hubert_loss
  from torch import Tensor
  from torch.optim.optimizer import Optimizer
  from torch.utils.data import DataLoader
label_desc: Use image node_id 1 for calling a global function with example usage: _compute_accuracy(logits) and returns: corr,  count