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: Multiplication of the contrast by a scalar
docstr_tok: ["Multiplication","of","the","contrast","by","a","scalar"]
code: def __rmul__(self, scalar):
      """Multiplication of the contrast by a scalar"""
      scalar = float(scalar)
      effect_ = self.effect * scalar
      variance_ = self.variance * scalar**2
      dof_ = self.dof
      return Contrast(
          effect=effect_,
          variance=variance_,
          dof=dof_,
          contrast_type=self.contrast_type,
      )
  
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 6 for calling the Contrast obj's underlying member method code with example usage: obj.__rmul__(scalar) and returns: Contrast