code: def compute_metrics(self, p: EvalPrediction) -> Dict:
      preds_list, out_label_list = self.align_predictions(
          p.predictions, p.label_ids
      )
      return {
          "precision": precision_score(out_label_list, preds_list),
          "recall": recall_score(out_label_list, preds_list),
          "f1": f1_score(out_label_list, preds_list),
      }
  
imports: import os
  import logging
  import torch
  import numpy
  from .data_ner import BertNERDataBunch
  from torch import nn
  from seqeval.metrics import f1_score, precision_score, recall_score
  from typing import Dict, List, Optional, Tuple
  from .learner_util import Learner
  from transformers import AutoConfig, AutoModelForTokenClassification, Trainer, TrainingArguments, EvalPrediction, HfArgumentParser
label_desc: Use image node_id 10 for calling the BertNERLearner obj's underlying member method code with example usage: obj.compute_metrics(p) and returns: dict