code: def make_masks(n_masks, n_dim0, n_dim1_min, rs=None):
      """Generate a set of masks, defined as semi-orthogonal matrices.
  
      Parameters
      ----------
      n_masks : int
          Number of masks to generate.
      n_dim0 : int
          First dimension of masks.
      n_dim1_min : int
          Minimal value for second dimension of masks.
      rs : RandomState instance, default=None
          Random state for reproducible output across multiple function calls.
  
      Returns
      -------
      masks : list of n_masks ndarray of shape (n_dim0, n_dim1_i), \
              with different n_dim1_i, such that n_dim1_min <= n_dim1_i <= n_dim0
          Masks.
  
      Notes
      -----
      .. versionadded:: 0.3
      """
      rs = check_random_state(rs)
  
      masks = []
      for _ in range(n_masks):
          n_dim1 = rs.randint(n_dim1_min, n_dim0, size=1)[0]
          mask, _ = np.linalg.qr(rs.randn(n_dim0, n_dim1))
          masks.append(mask)
      return masks
  
file_path: pyRiemann/pyriemann/datasets/simulated.py
imports: import numpy
  from sklearn.utils.validation import check_random_state
  from ..utils.mean import mean_riemann
  from ..utils.distance import distance_riemann
  from ..utils.base import invsqrtm, powm, sqrtm, expm
  from .sampling import generate_random_spd_matrix, sample_gaussian_spd
  from ..transfer import encode_domains
  from ..utils import deprecated
label_desc: Use image node_id 3 for calling a global function with example usage: make_masks(n_masks, n_dim0, n_dim1_min, rs) and returns: masks