code: def __init__(self, config, model):
      self.model = model
      self.criterion = nn.CrossEntropyLoss()
  
      bert_params = set(self.model.bert.parameters())
      other_params = list(set(self.model.parameters()) - bert_params)
      no_decay = ["bias", "LayerNorm.weight"]
      params = [
          {
              "params": [
                  p
                  for n, p in model.bert.named_parameters()
                  if not any(nd in n for nd in no_decay)
              ],
              "lr": config.bert_learning_rate,
              "weight_decay": config.weight_decay,
          },
          {
              "params": [
                  p
                  for n, p in model.bert.named_parameters()
                  if any(nd in n for nd in no_decay)
              ],
              "lr": config.bert_learning_rate,
              "weight_decay": 0.0,
          },
          {
              "params": other_params,
              "lr": config.learning_rate,
              "weight_decay": config.weight_decay,
          },
      ]
  
      self.optimizer = transformers.AdamW(
          params,
          lr=config.learning_rate,
          weight_decay=config.weight_decay,
      )
      self.scheduler = transformers.get_linear_schedule_with_warmup(
          self.optimizer,
          num_warmup_steps=config.warm_factor * config.updates_total,
          num_training_steps=config.updates_total,
      )
  
imports: import argparse
  import json
  import numpy
  import prettytable
  import torch
  import torch.autograd
  import torch.nn
  import transformers
  from sklearn.metrics import precision_recall_fscore_support, f1_score, classification_report
  from torch.utils.data import DataLoader
  import hydra
  from deepke.name_entity_re.standard.w2ner import *
  from deepke.name_entity_re.standard.w2ner.utils import *
  from deepke.name_entity_re.standard.tools import *
label_desc: Use image node_id 1 to create a new Trainer object from inherited base classes: object with example: obj = Trainer(config, model)