code: def _compute_fixed_effects_params(
      contrasts, variances, precision_weighted, dofs
  ):
      """Compute the fixed effects t/F-statistic, contrast, variance, \
      given arrays of effects and variance."""
      tiny = 1.0e-16
      contrasts, variances = np.asarray(contrasts), np.asarray(
          variances
      )
      variances = np.maximum(variances, tiny)
  
      if precision_weighted:
          weights = 1.0 / variances
          fixed_fx_variance = 1.0 / np.sum(weights, 0)
          fixed_fx_contrasts = (
              np.sum(contrasts * weights, 0) * fixed_fx_variance
          )
      else:
          fixed_fx_variance = np.mean(variances, 0) / len(variances)
          fixed_fx_contrasts = np.mean(contrasts, 0)
      dim = 1
      stat_type = "t"
      fixed_fx_contrasts_ = fixed_fx_contrasts
      if len(fixed_fx_contrasts.shape) == 2:
          dim = fixed_fx_contrasts.shape[0]
          if dim > 1:
              stat_type = "F"
      else:
          fixed_fx_contrasts_ = fixed_fx_contrasts[np.newaxis]
      con = Contrast(
          effect=fixed_fx_contrasts_,
          variance=fixed_fx_variance,
          dim=dim,
          dof=np.sum(dofs),
          stat_type=stat_type,
      )
      fixed_fx_z_score = con.z_score()
      fixed_fx_stat = con.stat_
  
      return (
          fixed_fx_contrasts,
          fixed_fx_variance,
          fixed_fx_stat,
          fixed_fx_z_score,
      )
  
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 a global function with example usage: _compute_fixed_effects_params(contrasts, variances, precision_weighted, dofs) and returns: fixed_fx_contrasts,  fixed_fx_variance,  fixed_fx_stat,  fixed_fx_z_score