code: def feature_matrix():
      num_features = 2
      num_samples = 10000
      means = np.random.uniform(-1, 1, num_features)
      # cov_mat = np.diag(np.ones(num_features))
      cov_mat = np.ones((num_features, num_features))
      cov_mat[0, 1] = 0.5
      cov_mat[1, 0] = 0.5
      X = np.random.multivariate_normal(means, cov_mat, num_samples)
      return X
  
imports: import numpy
  import pytest
  from matplotlib import pyplot
  from dowhy.data_transformers.pca_reducer import PCAReducer
label_desc: Use image node_id 1 for calling a global function with example usage: feature_matrix() and returns: X