code: def _hard_vote(y_pred, weights):
      n_samples, n_features = y_pred.shape
      maj = np.empty(n_samples, dtype=np.int64)
      for i in prange(n_samples):
          maj[i] = np.argmax(np.bincount(y_pred[i], weights))
      return maj
  
file_path: pyts/pyts/multivariate/classification/multivariate.py
imports: import numpy
  from numba import njit, prange
  from sklearn.base import BaseEstimator, clone
  from sklearn.preprocessing import LabelEncoder
  from sklearn.utils.validation import check_is_fitted
  from ...base import MultivariateClassifierMixin
  from ..utils import check_3d_array
label_desc: Use image node_id 1 for calling a global function with example usage: _hard_vote(y_pred, weights) and returns: maj