code: def _make_data(n_samples=1000, n_features=5, n_targets=3):
      """Generate some testing data.
  
      Parameters
      ----------
      n_samples : int
          The number of samples.
      n_features : int
          The number of features.
      n_targets : int
          The number of targets.
  
      Returns
      -------
      X : ndarray, shape (n_samples, n_features)
          The measured data.
      Y : ndarray, shape (n_samples, n_targets)
          The latent variables generating the data.
      A : ndarray, shape (n_features, n_targets)
          The forward model, mapping the latent variables (=Y) to the measured
          data (=X).
      """
      # Define Y latent factors
      np.random.seed(0)
      cov_Y = np.eye(n_targets) * 10 + np.random.rand(
          n_targets, n_targets
      )
      cov_Y = (cov_Y + cov_Y.T) / 2.0
      mean_Y = np.random.rand(n_targets)
      Y = np.random.multivariate_normal(mean_Y, cov_Y, size=n_samples)
  
      # The Forward model
      A = np.random.randn(n_features, n_targets)
  
      X = Y.dot(A.T)
      X += np.random.randn(n_samples, n_features)  # add noise
      X += np.random.rand(n_features)  # Put an offset
  
      return X, Y, A
  
imports: import numpy
  import pytest
  from numpy.testing import assert_allclose, assert_array_almost_equal, assert_array_equal, assert_array_less, assert_equal
  from mne import EpochsArray, create_info
  from mne.decoding import GeneralizingEstimator, Scaler, TransformerMixin, Vectorizer
  from mne.decoding.base import BaseEstimator, LinearModel, _get_inverse_funcs, cross_val_multiscore, get_coef
  from mne.decoding.search_light import SlidingEstimator
label_desc: Use image node_id 1 for calling a global function with example usage: _make_data(n_samples, n_features, n_targets) and returns: X,  Y,  A