class_docstr: This analysis allows maximum likelihood estimation of regression models
  in the presence of both spatial and temporal covariance.
  
  ..math::
      Y \sim \mathcal{MN}(Xeta, time_cov, space_cov)
  
  Parameters
  ----------
  time_cov : subclass of CovBase
      TR noise covariance class following CovBase interface.
  space_cov : subclass of CovBase
      Voxel noise covariance class following CovBase interface.
  optimizer : string, default="L-BFGS-B"
      Scipy optimizer to use. For other options, see "method" argument
      of scipy.optimize.minimize
  optCtrl: dict, default=None
      Additional arguments to pass to scipy.optimize.minimize.
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code: def __init__(
      self, time_cov, space_cov, optimizer="L-BFGS-B", optCtrl=None
  ):
      self.optMethod = optimizer
      if optCtrl is None:
          self.optCtrl = {}
  
      self.time_cov = time_cov
      self.space_cov = space_cov
  
      self.n_t = time_cov.size
      self.n_v = space_cov.size
  
file_path: brainiak/brainiak/matnormal/regression.py
imports: import tensorflow
  import numpy
  from sklearn.base import BaseEstimator
  from brainiak.matnormal.matnormal_likelihoods import matnorm_logp
  from brainiak.matnormal.utils import pack_trainable_vars, unpack_trainable_vars, make_val_and_grad
  from scipy.optimize import minimize
label_desc: Use image node_id 1 to create a new MatnormalRegression object from inherited base classes: BaseEstimator with example: obj = MatnormalRegression(time_cov, space_cov, optimizer, optCtrl)