code: def data_generator(
      self,
      batch_size: int = 1,
      phase: str = "train",
      shuffle: bool = True,
      data: List = None,
      return_list: bool = True,
  ) -> Callable:
      if phase != "predict" and not self.dataset:
          raise ValueError("The dataset is None ! It isn't allowed.")
      if phase == "train":
          shuffle = True
          examples = self.get_train_examples()
          self.num_examples["train"] = len(examples)
      elif phase == "val" or phase == "dev":
          shuffle = False
          examples = self.get_dev_examples()
          self.num_examples["dev"] = len(examples)
      elif phase == "test":
          shuffle = False
          examples = self.get_test_examples()
          self.num_examples["test"] = len(examples)
      elif phase == "predict":
          shuffle = False
          examples = []
          seq_id = 0
  
          for item in data:
              # set label in order to run the program
              if self.dataset:
                  label = list(self.label_map.keys())[0]
              else:
                  label = 0
              if len(item) == 1:
                  item_i = InputExample(
                      guid=seq_id, text_a=item[0], label=label
                  )
              elif len(item) == 2:
                  item_i = InputExample(
                      guid=seq_id,
                      text_a=item[0],
                      text_b=item[1],
                      label=label,
                  )
              else:
                  raise ValueError(
                      "The length of input_text is out of handling, which must be 1 or 2!"
                  )
              examples.append(item_i)
              seq_id += 1
      else:
          raise ValueError(
              "Unknown phase, which should be in ['train', 'dev', 'test', 'predict']."
          )
  
      def wrapper():
          if shuffle:
              np.random.shuffle(examples)
  
          for batch_data in self._prepare_batch_data(
              examples, batch_size, phase=phase
          ):
              if return_list:
                  # for DataFeeder
                  yield [batch_data]
              else:
                  # for DataLoader
                  yield batch_data
  
      return wrapper
  
imports: from collections import namedtuple
  from typing import Callable, Generator, Generic, List
  import numpy
  from paddlehub.utils.log import logger
  from paddlehub.compat.task import tokenization
  from paddlehub.compat.task.batch import pad_batch_data
label_desc: Use image node_id 6 for calling the BaseNLPReader obj's underlying member method code with example usage: obj.data_generator(batch_size, phase, shuffle, data, return_list) and returns: wrapper