text: from preprocessing import giveout_words_labels
  from keras.preprocessing.sequence import pad_sequences
  import torch
  import tensorflow as tf
  from transformers import BertTokenizer
  text=giveout_words_labels()
  text_train=text[0]
  text_test=text[2]
  
  if torch.cuda.is_available():
      device = torch.device("cuda") 
      print('There are %d GPU(s) available.' % torch.cuda.device_count()) 
      print('We will use the GPU:', torch.cuda.get_device_name(0))
  else:
      print('No GPU available, using the CPU instead.')
      device = torch.device("cpu")
  
  tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', do_lower_case=True)
  
  def tokenized():
      input_ids_train = []
      for sent in text_train:
          encoded_sent = tokenizer.encode(sent,add_special_tokens = True)
          input_ids_train.append(encoded_sent)
  
  
      input_ids_test = []
      for sent in text_test:
          encoded_sent = tokenizer.encode(sent,add_special_tokens = True)
          input_ids_test.append(encoded_sent)
  
      MAX_LEN = 32
      input_ids_train = pad_sequences(input_ids_train, maxlen=MAX_LEN, dtype="long",value=0, truncating="post", padding="post")
      input_ids_test = pad_sequences(input_ids_test, maxlen=MAX_LEN, dtype="long",value=0, truncating="post", padding="post")
  
      attention_masks_train = []
      for sent in input_ids_train:
          att_mask = [int(token_id > 0) for token_id in sent]
          attention_masks_train.append(att_mask)
      attention_masks_test = []
      for sent in input_ids_test:
          att_mask = [int(token_id > 0) for token_id in sent]
          attention_masks_test.append(att_mask)
  
      return input_ids_train,attention_masks_train,input_ids_test,attention_masks_test