code: def predict(
      self,
      predict_dataset,
      predict_examples,
      ignore_keys=None,
      metric_key_prefix: str = "test",
  ):
      predict_dataloader = self.get_test_dataloader(predict_dataset)
  
      # Temporarily disable metric computation, we will do it in the loop here.
      compute_metrics = self.compute_metrics
      self.compute_metrics = None
      eval_loop = (
          self.prediction_loop
          if self.args.use_legacy_prediction_loop
          else self.evaluation_loop
      )
      start_time = time.time()
      try:
          output = eval_loop(
              predict_dataloader,
              description="Prediction",
              # No point gathering the predictions if there are no metrics, otherwise we defer to
              # self.args.prediction_loss_only
              prediction_loss_only=True
              if compute_metrics is None
              else None,
              ignore_keys=ignore_keys,
              metric_key_prefix=metric_key_prefix,
          )
      finally:
          self.compute_metrics = compute_metrics
      total_batch_size = (
          self.args.eval_batch_size * self.args.world_size
      )
      if f"{metric_key_prefix}_jit_compilation_time" in output.metrics:
          start_time += output.metrics[
              f"{metric_key_prefix}_jit_compilation_time"
          ]
      output.metrics.update(
          speed_metrics(
              metric_key_prefix,
              start_time,
              num_samples=output.num_samples,
              num_steps=math.ceil(
                  output.num_samples / total_batch_size
              ),
          )
      )
  
      if (
          self.post_process_function is None
          or self.compute_metrics is None
      ):
          return output
  
      predictions = self.post_process_function(
          predict_examples,
          predict_dataset,
          output.predictions,
          "predict",
      )
      metrics = self.compute_metrics(predictions)
  
      # Prefix all keys with metric_key_prefix + '_'
      for key in list(metrics.keys()):
          if not key.startswith(f"{metric_key_prefix}_"):
              metrics[f"{metric_key_prefix}_{key}"] = metrics.pop(key)
      metrics.update(output.metrics)
      return PredictionOutput(
          predictions=predictions.predictions,
          label_ids=predictions.label_ids,
          metrics=metrics,
      )
  
file_path: adapters/examples/pytorch/question-answering/trainer_qa.py
imports: import math
  import time
  from adapters.trainer import AdapterTrainer
  from transformers import Trainer, is_torch_tpu_available
  from transformers.trainer_utils import PredictionOutput, speed_metrics
label_desc: Use image node_id 3 for calling the QuestionAnsweringTrainer obj's underlying member method code with example usage: obj.predict(predict_dataset, predict_examples, ignore_keys, metric_key_prefix) and returns: PredictionOutput, output