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).
class_docstr_tok: ["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",")","."]
docstr: Multiply a contrast by a scalar.
docstr_tok: ["Multiply","a","contrast","by","a","scalar","."]
code: def __rmul__(self, scalar):
      """Multiply a 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_,
          stat_type=self.stat_type,
      )
  
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 10 for calling the Contrast obj's underlying member method code with example usage: obj.__rmul__(scalar) and returns: Contrast