code: def gen_brsa_data_matnorm_model(
      U, n_T, n_V, space_cov, time_cov, n_nureg
  ):
      n_C = U.shape[0]
      beta = rmn(U, space_cov)
      X = rmn(np.eye(n_T), np.eye(n_C))
      beta_0 = rmn(np.eye(n_nureg), space_cov)
      X_0 = rmn(np.eye(n_T), np.eye(n_nureg))
      Y_hat = X.dot(beta) + X_0.dot(beta_0)
      Y = Y_hat + rmn(time_cov, space_cov)
      sizes = {"n_C": n_C, "n_T": n_T, "n_V": n_V}
      train = {"beta": beta, "X": X, "Y": Y, "U": U, "X_0": X_0}
  
      return train, sizes
  
file_path: brainiak/tests/matnormal/test_matnormal_rsa.py
imports: from brainiak.matnormal.mnrsa import MNRSA
  from brainiak.utils.utils import cov2corr
  from brainiak.matnormal.covs import CovIdentity, CovDiagonal
  from scipy.stats import norm
  from brainiak.matnormal.utils import rmn
  import numpy
label_desc: Use image node_id 2 for calling a global function with example usage: gen_brsa_data_matnorm_model(U, n_T, n_V, space_cov, time_cov, n_nureg) and returns: train,  sizes