docstr: Run evaluation and return metrics.
  
  The calling script will be responsible for providing a method to compute metrics, as they are
  task-dependent.
  
  Args:
      eval_dataset: (Optional) Pass a dataset if you wish to override
      the one on the instance.
  Returns:
      A dict containing:
          - the eval loss
          - the potential metrics computed from the predictions
docstr_tok: ["Run","evaluation","and","return","metrics",".","The","calling","script","will","be","responsible","for","providing","a","method","to","compute","metrics",",","as","they","are","task-dependent",".","Args",":","eval_dataset",":","(","Optional",")","Pass","a","dataset","if","you","wish","to","override","the","one","on","the","instance",".","Returns",":","A","dict","containing",":","-","the","eval","loss","-","the","potential","metrics","computed","from","the","predictions"]
code: def evaluate(
      self,
      eval_dataset: Optional[Dataset] = None,
      ignore_keys: Optional[List[str]] = None,
      metric_key_prefix: str = "eval",
  ) -> Dict[str, float]:
      """
      Run evaluation and return metrics.
  
      The calling script will be responsible for providing a method to compute metrics, as they are
      task-dependent.
  
      Args:
          eval_dataset: (Optional) Pass a dataset if you wish to override
          the one on the instance.
      Returns:
          A dict containing:
              - the eval loss
              - the potential metrics computed from the predictions
      """
      eval_dataloader = self.get_eval_dataloader(eval_dataset)
  
      output = self._prediction_loop(
          eval_dataloader,
          description="Evaluation",
          prediction_loss_only=True
          if self.compute_metrics is None
          else None,
          metric_key_prefix=metric_key_prefix,
      )
  
      if self.args.store_best_model:
          self.store_best_model(output)
  
      self.log(output.metrics)
  
      if self.args.tpu_metrics_debug:
          # tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
          xm.master_print(met.metrics_report())
  
      return output.metrics
  
imports: import collections
  import logging
  import os
  from dataclasses import dataclass, field
  from typing import Callable, Dict, List, Optional, Tuple, Union
  import numpy
  import torch
  from torch.utils.data import DataLoader
  from torch.utils.data.dataset import Dataset
  from tqdm import tqdm
  from adapters import AdapterTrainer
  from transformers import DataCollator, EvalPrediction, PreTrainedModel, PreTrainedTokenizerBase, Trainer, TrainerCallback, TrainingArguments, is_torch_tpu_available
  from transformers.trainer_utils import PredictionOutput
label_desc: Use image node_id 2 for calling the DependencyParsingTrainer obj's underlying member method code with example usage: obj.evaluate(eval_dataset, ignore_keys, metric_key_prefix) and returns: output