code: def Accuracy(log_probabilities, targets, length=None):
      """Calculates the accuracy for predicted log probabilities and targets in a batch.
  
      Arguments
      ----------
      log_probabilities : tensor
          Predicted log probabilities (batch_size, time, feature).
      targets : tensor
          Target (batch_size, time).
      length : tensor
          Length of target (batch_size,).
  
      Example
      -------
      >>> probs = torch.tensor([[0.9, 0.1], [0.1, 0.9], [0.8, 0.2]]).unsqueeze(0)
      >>> acc = Accuracy(torch.log(probs), torch.tensor([1, 1, 0]).unsqueeze(0), torch.tensor([2/3]))
      >>> print(acc)
      (1.0, 2.0)
      """
      if length is not None:
          mask = length_to_mask(
              length * targets.shape[1],
              max_len=targets.shape[1],
          ).bool()
          if len(targets.shape) == 3:
              mask = mask.unsqueeze(2).repeat(1, 1, targets.shape[2])
  
      padded_pred = log_probabilities.argmax(-1)
  
      if length is not None:
          numerator = torch.sum(
              padded_pred.masked_select(mask)
              == targets.masked_select(mask)
          )
          denominator = torch.sum(mask)
      else:
          numerator = torch.sum(padded_pred == targets)
          denominator = targets.shape[1]
      return float(numerator), float(denominator)
  
imports: import torch
  from speechbrain.dataio.dataio import length_to_mask
label_desc: Use image node_id 1 for calling a global function with example usage: Accuracy(log_probabilities, targets, length) and returns: float,  float