code: def get_optimizer(self, optim_states=None, saved_optim_type=None):
      return get_bert_optimizer(
          [self.model],
          self.params["type_optimization"],
          self.params["learning_rate"],
          fp16=self.params.get("fp16"),
      )
  
imports: import os
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from collections import OrderedDict
  from tqdm import tqdm
  from pytorch_transformers.modeling_utils import CONFIG_NAME, WEIGHTS_NAME
  from pytorch_transformers.modeling_bert import BertPreTrainedModel, BertConfig, BertModel
  from pytorch_transformers.modeling_roberta import RobertaConfig, RobertaModel
  from pytorch_transformers.tokenization_bert import BertTokenizer
  from pytorch_transformers.tokenization_roberta import RobertaTokenizer
  from blink.common.ranker_base import BertEncoder, get_model_obj
  from blink.common.optimizer import get_bert_optimizer
  from blink.common.params import ENT_START_TAG, ENT_END_TAG, ENT_TITLE_TAG
label_desc: Use image node_id 6 for calling the CrossEncoderRanker obj's underlying member method code with example usage: obj.get_optimizer(optim_states, saved_optim_type) and returns: get_bert_optimizer