code: def features_normal_cov_uniform(
      n_samples: int = 200, n_features: int = 30, dtype="float64"
  ):
      """Normal features generator with uniform covariance
  
      An example of features obtained as samples of a centered Gaussian
      vector with a specific covariance matrix given by 0.5 * (U + U.T),
      where U is uniform on [0, 1] and diagonal filled by ones.
  
      Parameters
      ----------
      n_samples : `int`, default=200
          Number of samples
  
      n_features : `int`, default=30
          Number of features
  
      dtype : `{'float64', 'float32'}`, default='float64'
          Type of the arrays used.
  
      Returns
      -------
      output : `numpy.ndarray`, shape=(n_samples, n_features)
          n_samples realization of a Gaussian vector with the described
          covariance
      """
      C = np.random.uniform(size=(n_features, n_features), dtype=dtype)
      np.fill_diagonal(C, 1.0)
      cov = 0.5 * (C + C.T)
      features = np.random.multivariate_normal(
          np.zeros(n_features), cov, size=n_samples
      )
      if dtype != "float64":
          return features.astype(dtype)
      return features
  
imports: import numpy
  from scipy.linalg.special_matrices import toeplitz
label_desc: Use image node_id 1 for calling a global function with example usage: features_normal_cov_uniform(n_samples, n_features, dtype) and returns: features, features