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_batch(cls, texts):
      """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()
      output_labels_count = int(
          os.environ.get(
              "OUTPUT_LABELS_COUNT",
              len(predictor_model.learner.data.labels),
          )
      )
  
      print("output_labels_count", output_labels_count)
  
      predictions = predictor_model.predict_batch(texts)
      return cls.process_batch_results(
          texts, predictions, labels_count=output_labels_count
      )
  
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 3 for calling the ScoringService obj's underlying member method code with example usage: obj.predict_batch(cls, texts) and returns: cls