class_docstr: Module for calculate the overall one-step-forward prediction accuracy.
  
  Example
  -------
  >>> probs = torch.tensor([[0.9, 0.1], [0.1, 0.9], [0.8, 0.2]]).unsqueeze(0)
  >>> stats = AccuracyStats()
  >>> stats.append(torch.log(probs), torch.tensor([1, 1, 0]).unsqueeze(0), torch.tensor([2/3]))
  >>> acc = stats.summarize()
  >>> print(acc)
  0.5
docstr: This function is for updating the stats according to the prediction
  and target in the current 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,).
docstr_tok: ["This","function","is","for","updating","the","stats","according","to","the","prediction","and","target","in","the","current","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",",",")","."]
code: def append(self, log_probabilities, targets, length=None):
      """This function is for updating the stats according to the prediction
      and target in the current 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,).
      """
      numerator, denominator = Accuracy(
          log_probabilities, targets, length
      )
      self.correct += numerator
      self.total += denominator
  
imports: import torch
  from speechbrain.dataio.dataio import length_to_mask
label_desc: Use image node_id 2 for calling the AccuracyStats obj's underlying member method code with example usage: obj.append(log_probabilities, targets, length) without return types