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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def sample(self, bqm, **parameters):
 """Cutoff and sample from the provided binary quadratic model. Removes interactions smaller than a given cutoff. Isolated variables (after the cutoff) are also removed. Note that if the problem had isolated variables before the cutoff, they will also be affected. Args: bqm (:obj:`dimod.BinaryQuadraticModel`):
 Binary quadratic model to be sampled from. **parameters: Parameters for the sampling method, specified by the child sampler. Returns: :obj:`dimod.SampleSet` """

        child = self.child
        cutoff = self._cutoff
        cutoff_vartype = self._cutoff_vartype
        comp = self._comparison

        if cutoff_vartype is dimod.SPIN:
            original = bqm.spin
        else:
            original = bqm.binary

        # remove all of the interactions less than cutoff
        new = type(bqm)(original.linear,
                        ((u, v, bias)
                         for (u, v), bias in original.quadratic.items()
                         if not comp(abs(bias), cutoff)),
                        original.offset,
                        original.vartype)

        # next we check for isolated qubits and remove them, we could do this as
        # part of the construction but the assumption is there should not be
        # a large number in the 'typical' case
        isolated = [v for v in new if not new.adj[v]]
        new.remove_variables_from(isolated)

        if isolated and len(new) == 0:
            # in this case all variables are isolated, so we just put one back
            # to serve as the basis
            v = isolated.pop()
            new.linear[v] = original.linear[v]

        # get the samples from the child sampler and put them into the original vartype
        sampleset = child.sample(new, **parameters).change_vartype(bqm.vartype, inplace=True)

        # we now need to add the isolated back in, in a way that minimizes
        # the energy. There are lots of ways to do this but for now we'll just
        # do one
        if isolated:
            samples, variables = _restore_isolated(sampleset, bqm, isolated)
        else:
            samples = sampleset.record.sample
            variables = sampleset.variables

        vectors = sampleset.data_vectors
        vectors.pop('energy')  # we're going to recalculate the energy anyway

        return dimod.SampleSet.from_samples_bqm((samples, variables), bqm, **vectors)