docstr: Compute signal prediction on model gradient table given given fODF
  and GM/CSF volume fractions for each voxel.
  
  Parameters
  ----------
  gtab : GradientTable, optional
      The gradients for which the signal will be predicted. Use the
      model's gradient table if `None`. Default: None
  S0 : ndarray ([x, y, z]) or float, optional
      The non diffusion-weighted signal value for each voxel. If a float,
      the same value is used for each voxel. If `None`, 1 is used for
      each voxel. Default: None
  
  Returns
  -------
  pred_sig : ndarray ([x, y, z], N)
      The predicted signal.
docstr_tok: ["Compute","signal","prediction","on","model","gradient","table","given","given","fODF","and","GM\/CSF","volume","fractions","for","each","voxel",".","Parameters","--","--","--","--","--","gtab",":","GradientTable",",","optional","The","gradients","for","which","the","signal","will","be","predicted",".","Use","the","model","'s","gradient","table","if","`","None","`",".","Default",":","None","S0",":","ndarray","(","[","x",",","y",",","z","]",")","or","float",",","optional","The","non","diffusion-weighted","signal","value","for","each","voxel",".","If","a","float",",","the","same","value","is","used","for","each","voxel",".","If","`","None","`",",","1","is","used","for","each","voxel",".","Default",":","None","Returns","--","--","--","-","pred_sig",":","ndarray","(","[","x",",","y",",","z","]",",","N",")","The","predicted","signal","."]
code: def predict(self, gtab=None, S0=None):
      """
      Compute signal prediction on model gradient table given given fODF
      and GM/CSF volume fractions for each voxel.
  
      Parameters
      ----------
      gtab : GradientTable, optional
          The gradients for which the signal will be predicted. Use the
          model's gradient table if `None`. Default: None
      S0 : ndarray ([x, y, z]) or float, optional
          The non diffusion-weighted signal value for each voxel. If a float,
          the same value is used for each voxel. If `None`, 1 is used for
          each voxel. Default: None
  
      Returns
      -------
      pred_sig : ndarray ([x, y, z], N)
          The predicted signal.
  
      """
      model_params = self.model_params
  
      if gtab is None:
          gtab = self.model.gtab_orig
  
      pred_kernel = generate_kernel(
          gtab,
          self.model.sphere,
          self.model.wm_response,
          self.model.gm_response,
          self.model.csf_response,
      )
  
      if S0 is None:
          S0 = np.ones(model_params.shape[:-1] + (1,))
      elif isinstance(S0, np.ndarray):
          S0 = S0[..., None]
      else:
          S0 = S0 * np.ones(model_params.shape[:-1] + (1,))
  
      pred_sig = np.empty(model_params.shape[:-1] + (len(gtab.bvals),))
      for ijk in np.ndindex(model_params.shape[:-1]):
          pred_sig[ijk] = S0[ijk] * np.dot(
              pred_kernel, model_params[ijk]
          )
  
      return pred_sig
  
imports: import logging
  import warnings
  import numpy
  from dipy.core.geometry import vec2vec_rotmat
  from dipy.core.gradients import gradient_table, unique_bvals_tolerance, get_bval_indices
  from dipy.core.onetime import auto_attr
  from dipy.core.sphere import Sphere
  from dipy.data import get_sphere
  from dipy.reconst.shm import lazy_index, normalize_data
  from dipy.reconst.odf import OdfModel, OdfFit
  from dipy.reconst.csdeconv import AxSymShResponse
  from dipy.segment.mask import bounding_box, crop
  from dipy.sims.voxel import single_tensor, all_tensor_evecs
label_desc: Use image node_id 8 for calling the RumbaFit obj's underlying member method code with example usage: obj.predict(gtab, S0) and returns: pred_sig