code: def initialize_model(
      self, saved_suffix=None, continue_training=False
  ):
      model = self.model_class_fn(self.config, self.device)
  
      if saved_suffix:
          model, start_epoch = self.load_model_checkpoint(
              model, saved_suffix, continue_training=continue_training
          )
          return model, start_epoch
  
      return model, 0
  
imports: import sys
  import os
  import logging
  import random
  import numpy
  import torch
  from torch import nn
  from torch.optim import AdamW
  from util.multigpu_fused_adam import FusedAdam
  from torch.optim.lr_scheduler import LambdaLR
  from torch.utils.data import DataLoader
  import time
  from os.path import join
  from datetime import datetime
  import util
  from util.tensorize_coref import CorefDataProcessor, coref_collate_fn
  from util.tensorize_ner import NERDataProcessor, ner_collate_fn
  from util.tensorize_ere import EREDataProcessor, ere_collate_fn
  from metrics import CorefEvaluator, MentionEvaluator
  from models.model_coref import CorefModel
  from models.model_ner import NERModel
  from models.model_ere import EREModel
  import wandb
label_desc: Use image node_id 2 for calling the Runner obj's underlying member method code with example usage: obj.initialize_model(saved_suffix, continue_training) and returns: model,  int, model,  start_epoch