code: def multi_shell_fiber_response(
      sh_order,
      bvals,
      wm_rf,
      gm_rf,
      csf_rf,
      sphere=None,
      tol=20,
      btens=None,
  ):
      """Fiber response function estimation for multi-shell data.
  
      Parameters
      ----------
      sh_order : int
           Maximum spherical harmonics order.
      bvals : ndarray
          Array containing the b-values. Must be unique b-values, like outputted
          by `dipy.core.gradients.unique_bvals_tolerance`.
      wm_rf : (4, len(bvals)) ndarray
          Response function of the WM tissue, for each bvals.
      gm_rf : (4, len(bvals)) ndarray
          Response function of the GM tissue, for each bvals.
      csf_rf : (4, len(bvals)) ndarray
          Response function of the CSF tissue, for each bvals.
      sphere : `dipy.core.Sphere` instance, optional
          Sphere where the signal will be evaluated.
      tol : int, optional
          Tolerance gap for b-values clustering.
      btens : can be any of two options, optional
  
          1. an array of strings of shape (N,) specifying
             encoding tensor shape associated with all unique b-values
             separately. N corresponds to the number of unique b-values,
             including the b0. Options for elements in array: 'LTE',
             'PTE', 'STE', 'CTE' corresponding to linear, planar, spherical, and
             "cigar-shaped" tensor encoding.
          2. an array of shape (N,3,3) specifying the b-tensor of each unique
             b-values exactly. N corresponds to the number of unique b-values,
             including the b0.
  
      Returns
      -------
      MultiShellResponse
          MultiShellResponse object.
      """
      NUMPY_1_14_PLUS = Version(np.__version__) >= Version("1.14.0")
      rcond_value = None if NUMPY_1_14_PLUS else -1
  
      bvals = np.array(bvals, copy=True)
      if btens is None:
          btens = np.repeat(["LTE"], len(bvals))
      elif len(btens) != len(bvals):
          msg = """bvals and btens parameters must have the same dimension."""
          raise ValueError(msg)
      evecs = np.zeros((3, 3))
      z = np.array([0, 0, 1.0])
      evecs[:, 0] = z
      evecs[:2, 1:] = np.eye(2)
  
      n = np.arange(0, sh_order + 1, 2)
      m = np.zeros_like(n)
  
      if sphere is None:
          sphere = default_sphere
  
      big_sphere = sphere.subdivide()
      theta, phi = big_sphere.theta, big_sphere.phi
  
      B = shm.real_sh_descoteaux_from_index(
          m, n, theta[:, None], phi[:, None]
      )
      A = shm.real_sh_descoteaux_from_index(0, 0, 0, 0)
  
      response = np.empty([len(bvals), len(n) + 2])
  
      if bvals[0] < tol:
          gtab = GradientTable(big_sphere.vertices * 0, btens=btens[0])
          wm_response = single_tensor(
              gtab, wm_rf[0, 3], wm_rf[0, :3], evecs, snr=None
          )
          response[0, 2:] = np.linalg.lstsq(
              B, wm_response, rcond=rcond_value
          )[0]
  
          response[0, 1] = gm_rf[0, 3] / A
          response[0, 0] = csf_rf[0, 3] / A
  
          for i, bvalue in enumerate(bvals[1:]):
              gtab = GradientTable(
                  big_sphere.vertices * bvalue, btens=btens[i + 1]
              )
              wm_response = single_tensor(
                  gtab, wm_rf[i, 3], wm_rf[i, :3], evecs, snr=None
              )
              response[i + 1, 2:] = np.linalg.lstsq(
                  B, wm_response, rcond=rcond_value
              )[0]
  
              response[i + 1, 1] = (
                  gm_rf[i, 3] * np.exp(-bvalue * gm_rf[i, 0]) / A
              )
              response[i + 1, 0] = (
                  csf_rf[i, 3] * np.exp(-bvalue * csf_rf[i, 0]) / A
              )
  
          S0 = [csf_rf[0, 3], gm_rf[0, 3], wm_rf[0, 3]]
  
      else:
          warnings.warn(
              """No b0 given. Proceeding either way.""", UserWarning
          )
          for i, bvalue in enumerate(bvals):
              gtab = GradientTable(
                  big_sphere.vertices * bvalue, btens=btens[i]
              )
              wm_response = single_tensor(
                  gtab, wm_rf[i, 3], wm_rf[i, :3], evecs, snr=None
              )
              response[i, 2:] = np.linalg.lstsq(
                  B, wm_response, rcond=rcond_value
              )[0]
  
              response[i, 1] = (
                  gm_rf[i, 3] * np.exp(-bvalue * gm_rf[i, 0]) / A
              )
              response[i, 0] = (
                  csf_rf[i, 3] * np.exp(-bvalue * csf_rf[i, 0]) / A
              )
  
          S0 = [csf_rf[0, 3], gm_rf[0, 3], wm_rf[0, 3]]
  
      return MultiShellResponse(response, sh_order, bvals, S0=S0)
  
imports: from packaging.version import Version
  import numbers
  import warnings
  import numpy
  from dipy.core import geometry
  from dipy.core.gradients import GradientTable, gradient_table, unique_bvals_tolerance, get_bval_indices
  from dipy.data import default_sphere
  from dipy.reconst import shm
  from dipy.reconst.csdeconv import response_from_mask_ssst
  from dipy.reconst.dti import TensorModel, fractional_anisotropy, mean_diffusivity
  from dipy.reconst.multi_voxel import multi_voxel_fit
  from dipy.reconst.utils import _roi_in_volume, _mask_from_roi
  from dipy.sims.voxel import single_tensor
  from dipy.utils.optpkg import optional_package
label_desc: Use image node_id 5 for calling a global function with example usage: multi_shell_fiber_response(sh_order, bvals, wm_rf, gm_rf, csf_rf, sphere, tol, btens) and returns: MultiShellResponse