code: def response_from_mask(gtab, data, mask):
      """Computation of single-shell single-tissue (ssst) response
          function from a given mask.
  
      Parameters
      ----------
      gtab : GradientTable
      data : ndarray
          diffusion data
      mask : ndarray
          mask from where to compute the response function
  
      Returns
      -------
      response : tuple, (2,)
          (`evals`, `S0`)
      ratio : float
          The ratio between smallest versus largest eigenvalue of the response.
  
      Notes
      -----
      In CSD there is an important pre-processing step: the estimation of the
      fiber response function. In order to do this, we look for voxels with very
      anisotropic configurations. This information can be obtained by using
      csdeconv.mask_for_response_ssst() through a mask of selected voxels
      (see[1]_). The present function uses such a mask to compute the ssst
      response function.
  
      For the response we also need to find the average S0 in the ROI. This is
      possible using `gtab.b0s_mask()` we can find all the S0 volumes (which
      correspond to b-values equal 0) in the dataset.
  
      The `response` consists always of a prolate tensor created by averaging
      the highest and second highest eigenvalues in the ROI with FA higher than
      threshold. We also include the average S0s.
  
      We also return the `ratio` which is used for the SDT models.
  
      References
      ----------
      .. [1] Tournier, J.D., et al. NeuroImage 2004. Direct estimation of the
      fiber orientation density function from diffusion-weighted MRI
      data using spherical deconvolution
      """
      return response_from_mask_ssst(gtab, data, mask)
  
imports: import warnings
  import numpy
  import numbers
  from scipy.integrate import quad
  from scipy.special import lpn, gamma
  import scipy.linalg
  import scipy.linalg.lapack
  from dipy.data import small_sphere, get_sphere, default_sphere
  from dipy.core.geometry import cart2sphere
  from dipy.core.ndindex import ndindex
  from dipy.sims.voxel import single_tensor
  from dipy.reconst.multi_voxel import multi_voxel_fit
  from dipy.reconst.dti import TensorModel, fractional_anisotropy
  from dipy.reconst.shm import sph_harm_ind_list, real_sh_descoteaux_from_index, sph_harm_lookup, lazy_index, SphHarmFit, real_sh_descoteaux, sh_to_rh, forward_sdeconv_mat, SphHarmModel
  from dipy.reconst.utils import _roi_in_volume, _mask_from_roi
  from dipy.direction.peaks import peaks_from_model
  from dipy.core.geometry import vec2vec_rotmat
  from dipy.utils.deprecator import deprecate_with_version, deprecated_params
label_desc: Use image node_id 2 for calling a global function with example usage: response_from_mask(gtab, data, mask) and returns: response_from_mask_ssst