code: def _peaks_from_model_parallel_sub(args):
      (data_file_name, mask_file_name) = args[0]
      (start_pos, end_pos) = args[1]
      model = args[2]
      sphere = args[3]
      relative_peak_threshold = args[4]
      min_separation_angle = args[5]
      return_odf = args[6]
      return_sh = args[7]
      gfa_thr = args[8]
      normalize_peaks = args[9]
      sh_order = args[10]
      sh_basis_type = args[11]
      npeaks = args[12]
      B = args[13]
      invB = args[14]
  
      data = np.load(data_file_name, mmap_mode="r")[start_pos:end_pos]
      if mask_file_name is not None:
          mask = np.load(mask_file_name, mmap_mode="r")[
              start_pos:end_pos
          ]
      else:
          mask = None
  
      return peaks_from_model(
          model,
          data,
          sphere,
          relative_peak_threshold,
          min_separation_angle,
          mask,
          return_odf,
          return_sh,
          gfa_thr,
          normalize_peaks,
          sh_order,
          sh_basis_type,
          npeaks,
          B,
          invB,
          parallel=False,
          num_processes=None,
      )
  
imports: import tempfile
  from multiprocessing import Pool
  from itertools import repeat
  from os import path
  import numpy
  import scipy.optimize
  from dipy.reconst.odf import gfa
  from dipy.reconst.recspeed import local_maxima, remove_similar_vertices, search_descending
  from dipy.core.sphere import Sphere
  from dipy.data import default_sphere
  from dipy.core.ndindex import ndindex
  from dipy.reconst.shm import sh_to_sf_matrix
  from dipy.reconst.eudx_direction_getter import EuDXDirectionGetter
  from dipy.utils.multiproc import determine_num_processes
  from dipy.utils.deprecator import deprecated_params
label_desc: Use image node_id 5 for calling a global function with example usage: _peaks_from_model_parallel_sub(args) and returns: peaks_from_model