class_docstr: A very general abstract class representing a metric which can be
  accumulated.
docstr: Parameters
  ----------
  predictions : ``torch.Tensor``, required.
      A tensor of predictions.
  gold_labels : ``torch.Tensor``, required.
      A tensor corresponding to some gold label to evaluate against.
  mask: ``torch.Tensor``, optional (default = None).
      A mask can be passed, in order to deal with metrics which are
      computed over potentially padded elements, such as sequence labels.
code: def __call__(self, predictions, gold_labels, mask):
      """
      Parameters
      ----------
      predictions : ``torch.Tensor``, required.
          A tensor of predictions.
      gold_labels : ``torch.Tensor``, required.
          A tensor corresponding to some gold label to evaluate against.
      mask: ``torch.Tensor``, optional (default = None).
          A mask can be passed, in order to deal with metrics which are
          computed over potentially padded elements, such as sequence labels.
      """
      raise NotImplementedError
  
file_path: magnitude/pymagnitude/third_party/allennlp/training/metrics/metric.py
imports: from __future__ import absolute_import
  import torch
  from allennlp.common.registrable import Registrable
label_desc: Use image node_id 1 for calling the Metric obj's underlying member method code with example usage: obj.__call__(predictions, gold_labels, mask) without return types