class_docstr: Computes accuracy classification score with optional ignored labels.
  The accuracy score is defined as
  .. math::
      \text{accuracy}(y, \hat{y}) = \frac{1}{n} \sum_{i=0}^{n-1}
      \text{1}(\hat{y_i} == y_i)
  Parameters
  ----------
  axis : int, default=1
      The axis that represents classes
  name : str
      Name of this metric instance for display.
  output_names : list of str, or None
      Name of predictions that should be used when updating with update_dict.
      By default include all predictions.
  label_names : list of str, or None
      Name of labels that should be used when updating with update_dict.
      By default include all labels.
  ignore_labels : int or iterable of integers, optional
      If provided as not None, will ignore these labels during update.
  Examples
  --------
  >>> predicts = [mx.nd.array([[0.3, 0.7], [0, 1.], [0.4, 0.6]])]
  >>> labels   = [mx.nd.array([0, 1, 1])]
  >>> acc = mx.metric.Accuracy()
  >>> acc.update(preds = predicts, labels = labels)
  >>> print acc.get()
  ('accuracy', 0.6666666666666666)
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code: def __init__(
      self,
      axis=1,
      name="accuracy",
      output_names=None,
      label_names=None,
      ignore_labels=None,
  ):
      super(Accuracy, self).__init__(
          name,
          axis=axis,
          output_names=output_names,
          label_names=label_names,
      )
      self.axis = axis
      self.ignore_labels = np.array(ignore_labels).flatten()
  
imports: import numpy
  from mxnet import ndarray
label_desc: Use image node_id 1 to create a new Accuracy object from inherited base classes: EvalMetric with example: obj = Accuracy(axis, name, output_names, label_names, ignore_labels)