code: def accuracy(labels, predictions, weights=None):
      if weights is None:
          weights = mx.np.ones_like(labels)
      is_correct = mx.np.equal(labels, predictions)
      acc = (is_correct * weights).sum() / (weights.sum() + 1e-6)
      return acc.asnumpy().item()
  
imports: import os
  import time
  import shutil
  import logging
  import argparse
  import functools
  import collections
  import mxnet
  import numpy
  from mxnet.lr_scheduler import PolyScheduler
  from sklearn import metrics
  from pretraining_utils import ElectraMasker, get_pretrain_data_npz, get_pretrain_data_text, get_electra_pretraining_model
  from gluonnlp.utils.misc import repeat, grouper, set_seed, init_comm, logging_config, naming_convention
  from gluonnlp.models.electra import ElectraModel, ElectraForPretrain
  from gluonnlp.utils.parameter import clip_grad_global_norm
label_desc: Use image node_id 6 for calling a global function with example usage: accuracy(labels, predictions, weights) and returns: acc