code: def fit(self, epochs, lr, validate=True):
      self.training_arguments.learning_rate = lr
      self.training_arguments.num_train_epochs = epochs
      self.training_arguments.do_eval = validate
  
      self.get_trainer().train()
  
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 3 for calling the BertNERLearner obj's underlying member method code with example usage: obj.fit(epochs, lr, validate) without return types