code: def load_model(self, fname, cpu=False):
      if cpu:
          state_dict = torch.load(
              fname, map_location=lambda storage, location: "cpu"
          )
      else:
          state_dict = torch.load(fname)
      self.model.load_state_dict(state_dict)
  
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 2 for calling the CrossEncoderRanker obj's underlying member method code with example usage: obj.load_model(fname, cpu) without return types