code: def __init__(
      self,
      axis=1,
      name="masked_accuracy",
      output_names=None,
      label_names=None,
  ):
      super(MaskedAccuracy, self).__init__(
          name,
          axis=axis,
          output_names=output_names,
          label_names=label_names,
      )
      self.axis = axis
  
imports: import time
  import os
  import sys
  import logging
  import random
  import numpy
  import mxnet
  from mxnet.gluon.metric import EvalMetric
  import gluonnlp.data.batchify
  from gluonnlp.utils.misc import glob
  from gluonnlp.data.loading import NumpyDataset, DatasetLoader
  from gluonnlp.data.sampler import SplitSampler, FixedBucketSampler
  from create_pretraining_data import create_training_instances
label_desc: Use image node_id 1 to create a new MaskedAccuracy object from inherited base classes: EvalMetric with example: obj = MaskedAccuracy(axis, name, output_names, label_names)