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: Add two contrast, Yields an new Contrast instance.
  
  This should be used only on indepndent contrasts.
docstr_tok: ["Add","two","contrast",",","Yields","an","new","Contrast","instance",".","This","should","be","used","only","on","indepndent","contrasts","."]
code: def __add__(self, other):
      """Add two contrast, Yields an new Contrast instance.
  
      This should be used only on indepndent contrasts.
      """
      if self.stat_type != other.stat_type:
          raise ValueError(
              "The two contrasts do not have consistent type dimensions"
          )
      if self.dim != other.dim:
          raise ValueError(
              "The two contrasts do not have compatible dimensions"
          )
      dof_ = self.dof + other.dof
      if self.stat_type == "F":
          warn(
              "Running approximate fixed effects on F statistics.",
              category=UserWarning,
              stacklevel=2,
          )
      effect_ = self.effect + other.effect
      variance_ = self.variance + other.variance
      return Contrast(
          effect=effect_,
          variance=variance_,
          dim=self.dim,
          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 9 for calling the Contrast obj's underlying member method code with example usage: obj.__add__(other) and returns: Contrast