class_docstr: The contrast class handles the estimation     of statistical :term:`contrasts<contrast>`     on a given model: student (t) or Fisher (F).
  
  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 :term:`contrasts<contrast>`
  may lead to memory breakage).
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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, default=0.0
      Baseline value for the test statistic.
  
  
  Returns
  -------
  z_score : 1-d array, shape=(n_voxels,)
      statistical values, one per voxel
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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, default=0.0
              Baseline value for the test statistic.
  
  
          Returns
          -------
          z_score : 1-d array, shape=(n_voxels,)
              statistical values, one per voxel
  
          """
      if self.p_value_ is None or self.baseline != baseline:
          self.p_value_ = self.p_value(baseline)
      if self.one_minus_pvalue_ is None:
          self.one_minus_pvalue_ = self.one_minus_pvalue(baseline)
  
      # Avoid inf values kindly supplied by scipy.
      self.z_score_ = z_score(
          self.p_value_, one_minus_pvalue=self.one_minus_pvalue_
      )
      return self.z_score_
  
imports: from warnings import warn
  import numpy
  import pandas
  import scipy.stats
  from nilearn._utils import rename_parameters
  from nilearn.glm._utils import pad_contrast, z_score
  from nilearn.maskers import NiftiMasker
label_desc: Use image node_id 8 for calling the Contrast obj's underlying member method code with example usage: obj.z_score(baseline) and returns: self