class_docstr: This class is intended for the Synb0 model.
  The model is the deep learning part of the Synb0-Disco
  pipeline, thus stand-alone usage is not
  recommended.
docstr: Load the model pre-training weights to use for the fitting.
  While the user can load different weights, the function
  is mainly intended for the class function 'predict'.
  
  Parameters
  ----------
  idx : int
      The idx of the default weights. It can be from 0~4.
docstr_tok: ["Load","the","model","pre-training","weights","to","use","for","the","fitting",".","While","the","user","can","load","different","weights",",","the","function","is","mainly","intended","for","the","class","function","'predict","'",".","Parameters","--","--","--","--","--","idx",":","int","The","idx","of","the","default","weights",".","It","can","be","from","0~4","."]
code: def fetch_default_weights(self, idx):
      r"""
      Load the model pre-training weights to use for the fitting.
      While the user can load different weights, the function
      is mainly intended for the class function 'predict'.
  
      Parameters
      ----------
      idx : int
          The idx of the default weights. It can be from 0~4.
      """
      fetch_model_weights_path = get_fnames("synb0_default_weights")
      print("fetched " + fetch_model_weights_path[idx])
      self.load_model_weights(fetch_model_weights_path[idx])
  
imports: import logging
  import numpy
  from dipy.data import get_fnames
  from dipy.testing.decorators import doctest_skip_parser
  from dipy.utils.optpkg import optional_package
  from dipy.nn.utils import normalize, unnormalize, set_logger_level
label_desc: Use image node_id 2 for calling the Synb0 obj's underlying member method code with example usage: obj.fetch_default_weights(idx) without return types