code: def sample(self, bqm, init_solution=None, tenure=None, scale_factor=1, timeout=20, num_reads=1):
          if init_solution is not None:
              if not isinstance(init_solution, dimod.SampleSet):
                  raise TypeError("'init_solution' should be a 'dimod.SampleSet' instance")
              if len(init_solution.record) < 1:
                  raise ValueError("'init_solution' should contain at least one sample")
              if len(init_solution.record[0].sample) != len(bqm):
                  raise ValueError("'init_solution' sample dimension different from BQM")
              init_sample = self._bqm_sample_to_tabu_sample(
                  init_solution.change_vartype(dimod.BINARY, inplace=False).record[0].sample, bqm.binary)
          else:
              init_sample = None
          if not bqm:
              return dimod.SampleSet.from_samples([], energy=0, vartype=bqm.vartype)
          if tenure is None:
              tenure = max(min(20, len(bqm) // 4), 0)
          if not isinstance(tenure, int):
              raise TypeError("'tenure' should be an integer in range [0, num_vars - 1]")
          if not 0 <= tenure < len(bqm):
              raise ValueError("'tenure' should be an integer in range [0, num_vars - 1]")
          if not isinstance(num_reads, int):
              raise TypeError("'num_reads' should be a positive integer")
          if num_reads < 1:
              raise ValueError("'num_reads' should be a positive integer")
          qubo = self._bqm_to_tabu_qubo(bqm.binary)
          samples = []
          energies = []
          for _ in range(num_reads):
              if init_sample is None:
                  init_sample = self._bqm_sample_to_tabu_sample(self._random_sample(bqm.binary), bqm.binary)
              r = TabuSearch(qubo, init_sample, tenure, scale_factor, timeout)
              sample = self._tabu_sample_to_bqm_sample(list(r.bestSolution()), bqm.binary)
              energy = bqm.binary.energy(sample)
              samples.append(sample)
              energies.append(energy)
          response = dimod.SampleSet.from_samples(
              samples, energy=energies, vartype=dimod.BINARY)
          response.change_vartype(bqm.vartype, inplace=True)
          return response