docstr: Predict the signal based on the SFM parameters
  
  Parameters
  ----------
  gtab : GradientTable, optional
      The bvecs/bvals to predict the signal on. Default: the gtab from
      the model object.
  
  response : list of 3 elements, optional
      The eigenvalues of a tensor which will serve as a kernel
      function. Default: the response of the model object. Default to use
      `model.response`.
  
  S0 : float or array, optional
       The non-diffusion-weighted signal. Default: use the S0 of the data
  
  Returns
  -------
  pred_sig : ndarray
      The signal predicted in each voxel/direction
docstr_tok: ["Predict","the","signal","based","on","the","SFM","parameters","Parameters","--","--","--","--","--","gtab",":","GradientTable",",","optional","The","bvecs\/bvals","to","predict","the","signal","on",".","Default",":","the","gtab","from","the","model","object",".","response",":","list","of","3","elements",",","optional","The","eigenvalues","of","a","tensor","which","will","serve","as","a","kernel","function",".","Default",":","the","response","of","the","model","object",".","Default","to","use","`","model.response","`",".","S0",":","float","or","array",",","optional","The","non-diffusion-weighted","signal",".","Default",":","use","the","S0","of","the","data","Returns","--","--","--","-","pred_sig",":","ndarray","The","signal","predicted","in","each","voxel\/direction"]
code: def predict(self, gtab=None, response=None, S0=None):
      """
      Predict the signal based on the SFM parameters
  
      Parameters
      ----------
      gtab : GradientTable, optional
          The bvecs/bvals to predict the signal on. Default: the gtab from
          the model object.
  
      response : list of 3 elements, optional
          The eigenvalues of a tensor which will serve as a kernel
          function. Default: the response of the model object. Default to use
          `model.response`.
  
      S0 : float or array, optional
           The non-diffusion-weighted signal. Default: use the S0 of the data
  
      Returns
      -------
      pred_sig : ndarray
          The signal predicted in each voxel/direction
      """
      if response is None:
          response = self.model.response
      if gtab is None:
          _matrix = self.model.design_matrix
          gtab = self.model.gtab
      # The only thing we can't change at this point is the sphere we use
      # (which sets the width of our design matrix):
      else:
          _matrix = sfm_design_matrix(gtab, self.model.sphere, response)
      # Get them all at once:
      pred_weighted = np.dot(
          _matrix, self.beta.reshape(-1, self.beta.shape[-1]).T
      ).T.reshape(self.beta.shape[:-1] + (_matrix.shape[0],))
  
      if S0 is None:
          S0 = self.S0
      if isinstance(S0, np.ndarray):
          S0 = S0[..., None]
  
      pre_pred_sig = S0 * (
          pred_weighted
          + self.iso.predict(gtab).reshape(pred_weighted.shape)
      )
      pred_sig = np.zeros(
          pre_pred_sig.shape[:-1] + (gtab.bvals.shape[0],)
      )
      pred_sig[..., ~gtab.b0s_mask] = pre_pred_sig
      pred_sig[..., gtab.b0s_mask] = S0
      return pred_sig.squeeze()
  
imports: import warnings
  import numpy
  import gc
  from collections import OrderedDict
  from dipy.utils.optpkg import optional_package
  from dipy.utils.multiproc import determine_num_processes
  import dipy.core.gradients
  import dipy.core.optimize
  import dipy.sims.voxel
  import dipy.data
  from dipy.reconst.base import ReconstModel, ReconstFit
  from dipy.reconst.cache import Cache
  from dipy.core.onetime import auto_attr
label_desc: Use image node_id 3 for calling the SparseFascicleFit obj's underlying member method code with example usage: obj.predict(gtab, response, S0) and returns: pred_sig