class_docstr: BERT-based sequence classifier
docstr: cache the output of torch.cuda.is_available() 
docstr_tok: ["cache","the","output","of","torch.cuda.is_available","(",")"]
code: def cuda(self):
      """cache the output of torch.cuda.is_available()"""
  
      self.has_cuda = torch.cuda.is_available()
      return self.has_cuda
  
imports: import logging
  import warnings
  from collections.abc import Iterable
  from enum import Enum
  import torch
  from pytorch_pretrained_bert.tokenization import BertTokenizer
  from tqdm import tqdm
  import numpy
  import torch.nn
  from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
  from pytorch_pretrained_bert.modeling import BertForSequenceClassification
  from pytorch_pretrained_bert.optimization import BertAdam
  from interpret_text.experimental.common.utils_pytorch import get_device, move_to_device
  from interpret_text.experimental.common.constants import BertTokens
  from collections import namedtuple
  from cached_property import cached_property
label_desc: Use image node_id 2 for calling the BERTSequenceClassifier obj's underlying member method code with example usage: obj.cuda() and returns: self