code: def load_from_config(cp, model, **kwargs):
      
      name = cp.get('sampler', 'name')
      return samplers[name].from_config(cp, model, **kwargs)
docs: Loads a sampler from the given config file.
  
      This looks for a name in the section ``[sampler]`` to determine which
      sampler class to load. That sampler's ``from_config`` is then called.
  
      Parameters
      ----------
      cp : WorkflowConfigParser
          Config parser to read from.
      model : pycbc.inference.model
          Which model to pass to the sampler.
      \**kwargs :
          All other keyword arguments are passed directly to the sampler's
          ``from_config`` file.
  
      Returns
      -------
      sampler :
          The initialized sampler.