class_docstr: Class which implements the relation ranking model
  Args:
      pretrained_bert: pretrained transformer checkpoint path or key title (e.g. "bert-base-uncased")
      encoder_save_path: path to save the encoder checkpoint
      linear_save_path: path to save linear layer checkpoint
      bert_tokenizer_config_file: path to configuration file of transformer tokenizer
      device: cpu or gpu
code: def __init__(
      self,
      pretrained_bert: str = None,
      encoder_save_path: str = None,
      linear_save_path: str = None,
      bert_tokenizer_config_file: str = None,
      device: str = "gpu",
  ):
      super().__init__()
      self.pretrained_bert = pretrained_bert
      self.encoder_save_path = encoder_save_path
      self.linear_save_path = linear_save_path
      self.device = torch.device(
          "cuda"
          if torch.cuda.is_available() and device == "gpu"
          else "cpu"
      )
  
      # initialize parameters that would be filled later
      self.encoder, self.config, self.bert_config = None, None, None
      self.load()
  
      if Path(bert_tokenizer_config_file).is_file():
          vocab_file = str(expand_path(bert_tokenizer_config_file))
          tokenizer = AutoTokenizer(vocab_file=vocab_file)
      else:
          tokenizer = AutoTokenizer.from_pretrained(pretrained_bert)
      self.encoder.resize_token_embeddings(len(tokenizer) + 7)
  
imports: from logging import getLogger
  from pathlib import Path
  from typing import List, Dict, Tuple, Union, Any
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from torch import Tensor
  from transformers import AutoConfig, AutoModel, AutoTokenizer
  from deeppavlov.core.commands.utils import expand_path
  from deeppavlov.core.common.errors import ConfigError
  from deeppavlov.core.common.registry import register
  from deeppavlov.core.models.torch_model import TorchModel
label_desc: Use image node_id 1 to create a new NLLRanking object from inherited base classes: nn with example: obj = NLLRanking(pretrained_bert, encoder_save_path, linear_save_path, bert_tokenizer_config_file, device)