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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 inference(metric):
 """Inference function."""


    logging.info('Now we are doing BERT classification inference on %s!', ctx)
    model = BERTClassifier(bert, dropout=0.1, num_classes=len(task.get_labels()))
    model.hybridize(static_alloc=True)
    model.load_parameters(model_parameters, ctx=ctx)

    metric.reset()
    step_loss = 0
    tic = time.time()
    for batch_id, seqs in enumerate(dev_data):
        input_ids, valid_length, type_ids, label = seqs
        out = model(input_ids.as_in_context(ctx),
                    type_ids.as_in_context(ctx),
                    valid_length.astype('float32').as_in_context(ctx))

        ls = loss_function(out, label.as_in_context(ctx)).mean()

        step_loss += ls.asscalar()
        metric.update([label], [out])

        if (batch_id + 1) % (args.log_interval) == 0:
            log_inference(batch_id, len(dev_data), metric, step_loss, args.log_interval)
            step_loss = 0

    mx.nd.waitall()
    toc = time.time()
    total_num = dev_batch_size * len(dev_data)
    logging.info('Time cost=%.2fs, throughput=%.2fsamples/s', toc - tic, \
                 total_num / (toc - tic))