class_docstr: Just checks batch-equality of two tensors and computes an accuracy metric based on that.
  That is, if your prediction has shape (batch_size, dim_1, ..., dim_n), this metric considers that
  as a set of `batch_size` predictions and checks that each is *entirely* correct across the remaining dims.
  This means the denominator in the accuracy computation is `batch_size`, with the caveat that predictions
  that are totally masked are ignored (in which case the denominator is the number of predictions that have
  at least one unmasked element).
  
  This is similar to [`CategoricalAccuracy`](./categorical_accuracy.md), if you've already done a `.max()`
  on your predictions.  If you have categorical output, though, you should typically just use
  `CategoricalAccuracy`.  The reason you might want to use this instead is if you've done
  some kind of constrained inference and don't have a prediction tensor that matches the API of
  `CategoricalAccuracy`, which assumes a final dimension of size `num_classes`.
docstr: # Returns
  
  The accumulated accuracy.
code: def get_metric(self, reset: bool = False):
      """
      # Returns
  
      The accumulated accuracy.
      """
      if self._total_count > 0:
          accuracy = float(self._correct_count) / float(
              self._total_count
          )
      else:
          accuracy = 0.0
      if reset:
          self.reset()
      return accuracy
  
file_path: allennlp/allennlp/training/metrics/boolean_accuracy.py
imports: from typing import Optional
  import torch
  from allennlp.nn.util import dist_reduce_sum
  from allennlp.training.metrics.metric import Metric
label_desc: Use image node_id 3 for calling the BooleanAccuracy obj's underlying member method code with example usage: obj.get_metric(reset) and returns: accuracy