How to use the poap.strategy.EvalRecord function in POAP

def new_feval(self, params, extra_args=None):
        """Add a function evaluation record to the database.

        In addition to adding the record with status 'pending',
        we run the feval_callbacks on the new record.

        Args:
            params: Parameters to the objective function

        Returns:
            New EvalRecord object
        """
        record = EvalRecord(params, extra_args=extra_args, status='pending')
        self.fevals.append(record)
        logger.debug("Call new feval callbacks")
        for callback in self.feval_callbacks:
            callback(record)
        return record

"""

        if self.batch_size is None:  # First call to suggest
            self.batch_size = n_suggestions
            self.start(self.max_evals)

        # Set the tolerances pretending like we are running batch
        d, p = float(self.opt.dim), float(n_suggestions)
        self.strategy.failtol = p * int(max(np.ceil(d / p), np.ceil(4 / p)))

        # Now we can make suggestions
        x_w = []
        self.proposals = []
        for _ in range(n_suggestions):
            proposal = self.strategy.propose_action()
            record = EvalRecord(proposal.args, status="pending")
            proposal.record = record
            proposal.accept()  # This triggers all the callbacks

            # It is possible that pySOT proposes a previously evaluated point
            # when all variables are integers, so we just abort in this case
            # since we have likely converged anyway. See PySOT issue #30.
            x = list(proposal.record.params)  # From tuple to list
            x_unwarped, = self.space_x.unwarp(x)
            if x_unwarped in self.history:
                warnings.warn("pySOT proposed the same point twice")
                self.start(self.max_evals)
                return self.suggest(n_suggestions=n_suggestions)

            # NOTE: Append unwarped to avoid rounding issues
            self.history.append(copy(x_unwarped))
            self.proposals.append(proposal)

"""

        if self.batch_size is None:  # First call to suggest
            self.batch_size = n_suggestions
            self.start(self.max_evals)

        # Set the tolerances pretending like we are running batch
        d, p = float(self.opt.dim), float(n_suggestions)
        self.strategy.failtol = p * int(max(np.ceil(d / p), np.ceil(4 / p)))

        # Now we can make suggestions
        x_w = []
        self.proposals = []
        for _ in range(n_suggestions):
            proposal = self.strategy.propose_action()
            record = EvalRecord(proposal.args, status="pending")
            proposal.record = record
            proposal.accept()  # This triggers all the callbacks

            # It is possible that pySOT proposes a previously evaluated point
            # when all variables are integers, so we just abort in this case
            # since we have likely converged anyway. See PySOT issue #30.
            x = list(proposal.record.params)  # From tuple to list
            x_unwarped, = self.space_x.unwarp(x)
            if x_unwarped in self.history:
                warnings.warn("pySOT proposed the same point twice")
                self.start(self.max_evals)
                return self.suggest(n_suggestions=n_suggestions)

            # NOTE: Append unwarped to avoid rounding issues
            self.history.append(copy(x_unwarped))
            self.proposals.append(proposal)