class_docstr: The contrast class handles the estimation of statistical contrasts
  on a given model: student (t), Fisher (F), conjunction (tmin-conjunction).
  The important feature is that it supports addition,
  thus opening the possibility of fixed-effects models.
  
  The current implementation is meant to be simple,
  and could be enhanced in the future on the computational side
  (high-dimensional F contrasts may lead to memory breakage).
  
  Notes
  -----
  The 'tmin-conjunction' test is the valid conjunction test discussed in:
  Nichols T, Brett M, Andersson J, Wager T, Poline JB. Valid conjunction
  inference with the minimum statistic. Neuroimage. 2005 Apr 15;25(3):653-60.
  This test gives the p-value of the z-values under the conjunction null,
  i.e. the union of the null hypotheses for all terms.
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docstr: Return a parametric estimation of the z-score associated
  with the null hypothesis: (H0) 'contrast equals baseline'
  
  Parameters
  ----------
  baseline: float, optional
      Baseline value for the test statistic
  
  Notes
  -----
  The value of 0 is used where the stat is not defined
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code: def z_score(self, baseline=0.0):
      """Return a parametric estimation of the z-score associated
      with the null hypothesis: (H0) 'contrast equals baseline'
  
      Parameters
      ----------
      baseline: float, optional
          Baseline value for the test statistic
  
      Notes
      -----
      The value of 0 is used where the stat is not defined
      """
      if self.p_value_ is None or self.baseline != baseline:
          self.p_value_ = self.p_value(baseline)
  
      # Avoid inf values kindly supplied by scipy.
      self.z_score_ = z_score(self.p_value_)
      self.z_score_[np.isnan(self.stat_)] = 0
      return self.z_score_
  
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 4 for calling the Contrast obj's underlying member method code with example usage: obj.z_score(baseline) and returns: self