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 the decision statistic associated with the test of the         null hypothesis: (H0) 'contrast equals baseline'.
  
  Parameters
  ----------
  baseline : float, default=0.0
      Baseline value for the test statistic.
  
  Returns
  -------
  stat : 1-d array, shape=(n_voxels,)
      statistical values, one per voxel.
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code: def stat(self, baseline=0.0):
      """Return the decision statistic associated with the test of the \
          null hypothesis: (H0) 'contrast equals baseline'.
  
          Parameters
          ----------
          baseline : float, default=0.0
              Baseline value for the test statistic.
  
          Returns
          -------
          stat : 1-d array, shape=(n_voxels,)
              statistical values, one per voxel.
  
          """
      self.baseline = baseline
  
      # Case: one-dimensional contrast ==> t or t**2
      if self.stat_type == "F":
          stat = (
              np.sum((self.effect - baseline) ** 2, 0)
              / self.dim
              / np.maximum(self.variance, self.tiny)
          )
      elif self.stat_type == "t":
          # avoids division by zero
          stat = (self.effect - baseline) / np.sqrt(
              np.maximum(self.variance, self.tiny)
          )
      else:
          raise ValueError("Unknown statistic type")
      self.stat_ = stat.ravel()
      return self.stat_
  
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 5 for calling the Contrast obj's underlying member method code with example usage: obj.stat(baseline) and returns: self