docstr: Save the weights of the model to HDF5 file format.
  
  Parameters
  ----------
  filepath : str
      The path where the weights should be saved.
  overwrite : bool,optional
      If `True`, overwrites the file if it already exists. If `False`,
      raises an error if the file already exists.
docstr_tok: ["Save","the","weights","of","the","model","to","HDF5","file","format",".","Parameters","--","--","--","--","--","filepath",":","str","The","path","where","the","weights","should","be","saved",".","overwrite",":","bool",",","optional","If","`","True","`",",","overwrites","the","file","if","it","already","exists",".","If","`","False","`",",","raises","an","error","if","the","file","already","exists","."]
code: def save_weights(self, filepath, overwrite=True):
      """Save the weights of the model to HDF5 file format.
  
      Parameters
      ----------
      filepath : str
          The path where the weights should be saved.
      overwrite : bool,optional
          If `True`, overwrites the file if it already exists. If `False`,
          raises an error if the file already exists.
  
      """
      self.model.save_weights(
          filepath=filepath, overwrite=overwrite, save_format=None
      )
  
file_path: dipy/dipy/nn/cnn_1d_denoising.py
imports: from dipy.utils.optpkg import optional_package
  import numpy
label_desc: Use image node_id 8 for calling the Cnn1DDenoiser obj's underlying member method code with example usage: obj.save_weights(filepath, overwrite) without return types