docstr: Runs through the provided dataloader with torch.no_grad()
  :param dataloader: (torch.utils.data.DataLoader) Evaluation DataLoader
  :return: None
docstr_tok: ["Runs","through","the","provided","dataloader","with","torch.no_grad","(",")",":","param","dataloader",":","(","torch.utils.data.DataLoader",")","Evaluation","DataLoader",":","return",":","None"]
code: def evaluate(self, dataloader):
      """
      Runs through the provided dataloader with torch.no_grad()
      :param dataloader: (torch.utils.data.DataLoader) Evaluation DataLoader
      :return: None
      """
      loss_fn = nn.CrossEntropyLoss()
  
      if self.n_gpu > 1 and not isinstance(self.model, nn.DataParallel):
          self.model = nn.DataParallel(self.model)
  
      self.model.eval()
      eval_loss = 0.0
      perplexity = 0.0
      eval_steps = 0
  
      logging.info(f"{datetime.now()} | Evaluating...")
      for step, batch in tqdm(
          enumerate(dataloader),
          desc="Evaluating",
          leave=True,
          total=len(dataloader),
      ):
          for data in batch:
              inputs = self._tokenize_input_ids(
                  data, pad_to_max_length=True
              )
              inputs, labels = self.mask_tokens(inputs)
              inputs, labels = inputs.to(self.device), labels.to(
                  self.device
              )
  
              with torch.no_grad():
                  output = self.model(inputs)
  
              loss_mx = labels != -100
              output_ids = output[loss_mx].view(
                  -1, self.tokenizer.vocab_size
              )
              labels = labels[loss_mx].view(-1)
              tmp_eval_loss = loss_fn(output_ids, labels)
              tmp_perplexity = torch.exp(tmp_eval_loss)
  
              if self.n_gpu > 1:
                  tmp_eval_loss = tmp_eval_loss.mean()
  
              eval_loss += tmp_eval_loss.item()
              perplexity += tmp_perplexity.item()
              eval_steps += 1
  
          eval_loss /= eval_steps
          perplexity /= eval_steps
  
          if self.tb_writer:
              self.writer.add_scalar("Eval/Loss", eval_loss, eval_steps)
              self.writer.close()
              self.writer.add_scalar(
                  "Perplexity", perplexity, eval_steps
              )
              self.writer.close()
          logging.info(
              f"{datetime.now()} | Step: {step} | Eval Loss: {eval_loss} | Perplexity: {perplexity}"
          )
  
      return None
  
imports: import re
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.utils.data import Dataset, DataLoader, random_split
  from tqdm import tqdm
  from reformer_pytorch import Reformer, ReformerLM
  from transformers import BertTokenizer, PreTrainedTokenizer
  from fairseq.optim.adafactor import Adafactor
  import os
  import json
  import logging
  from datetime import datetime
label_desc: Use image node_id 6 for calling the ReformerTrainer obj's underlying member method code with example usage: obj.evaluate(dataloader) and returns: None