code: def _fisher_score(X, y, classes, class_counts):
      """Fisher score for feature selection."""
      a = 0
      b = 0
  
      x_mean = np.mean(X)
  
      for i, cls in enumerate(classes):
          X_cls = X[np.where(y == cls)]
          xy_mean = np.mean(X_cls)
          xy_std = np.std(X_cls)
  
          a += class_counts[i] * (xy_mean - x_mean) ** 2
          b += class_counts[i] * xy_std**2
  
      return 0 if b == 0 else a / b
  
imports: import math
  import numpy
  from joblib import Parallel, delayed
  from scipy import signal, stats
  from sklearn.base import clone
  from sklearn.preprocessing import StandardScaler
  from sklearn.tree import DecisionTreeClassifier
  from sklearn.utils.validation import check_random_state
  from sktime.classification.base import BaseClassifier
  from sktime.utils.slope_and_trend import _slope
label_desc: Use image node_id 1 for calling a global function with example usage: _fisher_score(X, y, classes, class_counts) and returns: unknown