class_docstr: This class handles the so-called on General Linear Model
  
  Most of what it does in the fit() and contrast() methods
  fit() performs the standard two-step ('ols' then 'ar1') GLM fitting
  contrast() returns a contrast instance, yileding statistics and p-values.
  The link between fit() and contrast is done vis the two class members:
  
  glm_results : dictionary of nipy.algorithms.statistics.models.
               regression.RegressionResults instances,
               describing results of a GLM fit
  
  labels : array of shape(n_voxels),
          labels that associate each voxel with a results key
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docstr: Accessor for the best linear unbiased estimated of model parameters
  
  Parameters
  ----------
  column_index: int or array-like of int or None, optional
      The indexed of the columns to be returned.  if None (default
      behaviour), the whole vector is returned
  
  Returns
  -------
  beta: array of shape (n_voxels, n_columns)
      the beta
docstr_tok: ["Accessor","for","the","best","linear","unbiased","estimated","of","model","parameters","Parameters","--","--","--","--","--","column_index",":","int","or","array-like","of","int","or","None",",","optional","The","indexed","of","the","columns","to","be","returned",".","if","None","(","default","behaviour",")",",","the","whole","vector","is","returned","Returns","--","--","--","-","beta",":","array","of","shape","(","n_voxels",",","n_columns",")","the","beta"]
code: def get_beta(self, column_index=None):
      """Accessor for the best linear unbiased estimated of model parameters
  
      Parameters
      ----------
      column_index: int or array-like of int or None, optional
          The indexed of the columns to be returned.  if None (default
          behaviour), the whole vector is returned
  
      Returns
      -------
      beta: array of shape (n_voxels, n_columns)
          the beta
      """
      # make colum_index a list if it an int
      if column_index is None:
          column_index = np.arange(self.X.shape[1])
      if not hasattr(column_index, "__iter__"):
          column_index = [int(column_index)]
      n_beta = len(column_index)
  
      # build the beta array
      beta = np.zeros((n_beta, self.labels_.size), dtype=np.float64)
      for l in self.results_:
          beta[:, self.labels_ == l] = self.results_[l].theta[
              column_index
          ]
      return beta
  
imports: from warnings import warn
  import numpy
  import scipy.stats
  from nibabel import Nifti1Image, load
  from nipy.algorithms.statistics.models.regression import ARModel, OLSModel
  from nipy.algorithms.statistics.utils import multiple_mahalanobis, z_score
  from nipy.io.nibcompat import get_affine, get_header
  from nipy.labs.mask import compute_mask_sessions
label_desc: Use image node_id 3 for calling the GeneralLinearModel obj's underlying member method code with example usage: obj.get_beta(column_index) and returns: beta