docstr: The input of the self.inference_tool.predict() is a list of sentence.
  The default tokenizer is based on white space (if tokenizer is None).
  The user can also customize their own tokenizer in defining the             ClassifierInferenceWrapper.
docstr_tok: ["The","input","of","the","self.inference_tool.predict","(",")","is","a","list","of","sentence",".","The","default","tokenizer","is","based","on","white","space","(","if","tokenizer","is","None",")",".","The","user","can","also","customize","their","own","tokenizer","in","defining","the","ClassifierInferenceWrapper","."]
code: def predict(self):
      """The input of the self.inference_tool.predict() is a list of sentence.
          The default tokenizer is based on white space (if tokenizer is None).
          The user can also customize their own tokenizer in defining the \
              ClassifierInferenceWrapper."""
      self.model.eval()
      ret = self.inference_tool.predict(
          raw_contents=["there is a list of jobs", "good morning"],
          batch_size=1,
      )
      print(ret)
  
imports: import argparse
  import os
  import torch
  import torch.backends.cudnn
  import torch.multiprocessing
  from graph4nlp.pytorch.data.dataset import SequenceLabelingDataItem
  from graph4nlp.pytorch.inference_wrapper.classifier_inference_wrapper import ClassifierInferenceWrapper
  from graph4nlp.pytorch.modules.config import get_basic_args
  from graph4nlp.pytorch.modules.utils.config_utils import get_yaml_config, update_values
  from graph4nlp.pytorch.modules.utils.generic_utils import to_cuda
  from conll import ConllDataset_inference
  from conlleval import evaluate
  from line_graph_construction import LineBasedGraphConstruction
  from model import Word2tag
label_desc: Use image node_id 3 for calling the Conll obj's underlying member method code with example usage: obj.predict() without return types