code: def get_model(parameters):
      model = BertForReranking.from_pretrained(
          parameters["path_to_model"],
          num_labels=parameters["top_k"],
          cache_dir=os.path.join(
              str(PYTORCH_PRETRAINED_BERT_CACHE), "local"
          ),
      )
  
      if parameters["dataparallel_bert"]:
          model.bert = torch.nn.DataParallel(model.bert)
          print("Data parallel Bert")
  
      return model
  
imports: import torch
  import os
  import numpy
  from pytorch_transformers.modeling_bert import BertPreTrainedModel, BertConfig, BertModel
  from pytorch_transformers.tokenization_bert import BertTokenizer
  from torch.utils.data import DataLoader, SequentialSampler, TensorDataset
  from torch import nn
  from torch.nn import CrossEntropyLoss, MSELoss
  from tqdm import tqdm
  from pytorch_transformers.optimization import AdamW, WarmupLinearSchedule
  from pytorch_transformers.file_utils import PYTORCH_PRETRAINED_BERT_CACHE
label_desc: Use image node_id 4 for calling the BertReranker obj's underlying member method code with example usage: obj.get_model(parameters) and returns: model