code: def _compute_expected_results(
      X,
      y=None,
      n_coefs=None,
      n_bins=4,
      strategy="quantile",
      drop_sum=False,
      anova=False,
      norm_mean=False,
      norm_std=False,
      alphabet=None,
  ):
      """Compute the expected results."""
      X = np.asarray(X)
      if norm_mean:
          X -= X.mean(axis=1)[:, None]
      if norm_std:
          X /= X.std(axis=1)[:, None]
      X_fft = np.fft.rfft(X)
      X_fft = np.vstack([np.real(X_fft), np.imag(X_fft)])
      X_fft = X_fft.reshape(n_samples, -1, order="F")
      if drop_sum:
          X_fft = X_fft[:, 2:-1]
      else:
          X_fft = np.hstack([X_fft[:, :1], X_fft[:, 2:-1]])
      if n_coefs is not None:
          if anova:
              _, p = f_classif(X_fft, y)
              support = np.argsort(p)[:n_coefs]
              X_fft = X_fft[:, support]
          else:
              X_fft = X_fft[:, :n_coefs]
  
      mcb = MultipleCoefficientBinning(
          n_bins=n_bins, strategy=strategy, alphabet=alphabet
      )
      arr_desired = mcb.fit_transform(X_fft)
      return arr_desired
  
imports: import numpy
  import pytest
  from sklearn.feature_selection import f_classif
  from pyts.approximation import MultipleCoefficientBinning
  from pyts.approximation import SymbolicFourierApproximation
label_desc: Use image node_id 1 for calling a global function with example usage: _compute_expected_results(X, y, n_coefs, n_bins, strategy, drop_sum, anova, norm_mean, norm_std, alphabet) and returns: arr_desired