docstr: For the input, do the predictions and return them.
  Args:
      input (a pandas dataframe): The data on which to do the predictions. There will be
          one prediction per row in the dataframe
docstr_tok: ["For","the","input",",","do","the","predictions","and","return","them",".","Args",":","input","(","a","pandas","dataframe",")",":","The","data","on","which","to","do","the","predictions",".","There","will","be","one","prediction","per","row","in","the","dataframe"]
code: def predict(cls, text, bing_key=None):
      """For the input, do the predictions and return them.
      Args:
          input (a pandas dataframe): The data on which to do the predictions. There will be
              one prediction per row in the dataframe"""
      predictor_model = cls.get_predictor_model()
      if bing_key:
          spellChecker = BingSpellCheck(bing_key)
          text = spellChecker.spell_check(text)
      prediction = predictor_model.predict(text)
  
      return prediction
  
imports: import os
  import io
  import json
  import pickle
  import sys
  import signal
  import traceback
  import re
  import flask
  import pandas
  import torch
  from collections import OrderedDict
  from fast_bert.prediction import BertClassificationPredictor
  from fast_bert.utils.spellcheck import BingSpellCheck
  from pathlib import Path
  import warnings
label_desc: Use image node_id 2 for calling the ScoringService obj's underlying member method code with example usage: obj.predict(cls, text, bing_key) and returns: prediction