code: def round_accuracy(y_true, y_predicted):
      """
      Rounds predictions and calculates accuracy in terms of absolute coincidence.
  
      Args:
          y_true: list of true values
          y_predicted: list of predicted values
  
      Returns:
          portion of absolutely coincidental samples
      """
      if isinstance(y_predicted[0], np.ndarray):
          predictions = [np.round(x) for x in y_predicted]
      else:
          predictions = [round(x) for x in y_predicted]
      examples_len = len(y_true)
      correct = sum([y1 == y2 for y1, y2 in zip(y_true, predictions)])
      return correct / examples_len if examples_len else 0
  
file_path: DeepPavlov/deeppavlov/metrics/accuracy.py
imports: import itertools
  import re
  from logging import getLogger
  from typing import List
  import numpy
  from deeppavlov.core.common.metrics_registry import register_metric
label_desc: Use image node_id 10 for calling a global function with example usage: round_accuracy(y_true, y_predicted) and returns: unknown