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 estimate of the p-value associated with         the null hypothesis (H0): 'contrast equals baseline',         using the survival function.
  
  Parameters
  ----------
  baseline : float, default=0.0
      Baseline value for the test statistic.
  
  
  Returns
  -------
  p_values : 1-d array, shape=(n_voxels,)
      p-values, one per voxel
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code: def p_value(self, baseline=0.0):
      """Return a parametric estimate of the p-value associated with \
          the null hypothesis (H0): 'contrast equals baseline', \
          using the survival function.
  
          Parameters
          ----------
          baseline : float, default=0.0
              Baseline value for the test statistic.
  
  
          Returns
          -------
          p_values : 1-d array, shape=(n_voxels,)
              p-values, one per voxel
  
          """
      if self.stat_ is None or self.baseline != baseline:
          self.stat_ = self.stat(baseline)
      # Valid conjunction as in Nichols et al, Neuroimage 25, 2005.
      if self.stat_type == "t":
          p_values = sps.t.sf(
              self.stat_, np.minimum(self.dof, self.dofmax)
          )
      elif self.stat_type == "F":
          p_values = sps.f.sf(
              self.stat_, self.dim, np.minimum(self.dof, self.dofmax)
          )
      else:
          raise ValueError("Unknown statistic type")
      self.p_value_ = p_values
      return p_values
  
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 6 for calling the Contrast obj's underlying member method code with example usage: obj.p_value(baseline) and returns: p_values