code: def inference(metric):
      
  
      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))
docs: Inference function.