code: def _restore_isolated(sampleset, bqm, isolated):
      
  
      samples = sampleset.record.sample
      variables = sampleset.variables
  
      new_samples = np.empty((len(sampleset), len(isolated)), dtype=samples.dtype)
  
      # we don't let the isolated variables interact with each other for now because
      # it will slow this down substantially
      for col, v in enumerate(isolated):
          try:
              neighbours, biases = zip(*((u, bias) for u, bias in bqm.adj[v].items()
                                         if u in variables))  # ignore other isolates
          except ValueError:
              # happens when only neighbors are other isolated variables
              new_samples[:, col] = bqm.linear[v] <= 0
              continue
  
          idxs = [variables.index[u] for u in neighbours]
  
          # figure out which value for v would minimize the energy
          # v(h_v + \sum_u J_uv * u)
          new_samples[:, col] = samples[:, idxs].dot(biases) < -bqm.linear[v]
  
      if bqm.vartype is dimod.SPIN:
          new_samples = 2*new_samples - 1
  
      return np.concatenate((samples, new_samples), axis=1), list(variables) + isolated
docs: Return samples-like by adding isolated variables into sampleset in a
      way that minimizes the energy (relative to the other non-isolated variables).