code: def transformation():
      """Do an inference on a single batch of data. In this sample server, we take data as CSV, convert
      it to a pandas data frame for internal use and then convert the predictions back to CSV (which really
      just means one prediction per line, since there's a single column.
      """
      data = None
      text = None
  
      if flask.request.content_type == "application/json":
          print("calling json launched")
          data = flask.request.get_json(silent=True)
  
          text = data["text"]
          try:
              bing_key = data["bing_key"]
          except Exception:
              bing_key = None
  
          # Do the prediction
          predictions = ScoringService.predict(text, bing_key)
          result = json.dumps(predictions[:10])
          return flask.Response(
              response=result, status=200, mimetype="application/json"
          )
  
      elif flask.request.content_type == "text/csv":
          data = flask.request.data.decode("utf-8")
          df = pd.read_csv(io.StringIO(data), header="infer")
          predictions = ScoringService.predict_batch(
              list(df["text"].values)
          )
  
          out = io.StringIO()
          pd.DataFrame(predictions).to_csv(out, index=False)
          result = out.getvalue()
          return flask.Response(
              response=result, status=200, mimetype="text/csv"
          )
  
      elif flask.request.content_type == "text/plain":
          data = flask.request.data.decode("utf-8")
          s = io.StringIO(data)
          # convert txt file into list of texts
          texts = []
          for line in s:
              texts.append(line)
          predictions = ScoringService.predict_batch(texts)
          out = io.StringIO()
          pd.DataFrame(predictions).to_csv(out, index=False)
          result = out.getvalue()
          return flask.Response(
              response=result, status=200, mimetype="text/csv"
          )
  
      else:
          return flask.Response(
              response="This predictor only supports JSON, txt or CSV data",
              status=415,
              mimetype="text/plain",
          )
  
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 a global function with example usage: transformation() and returns: flask, flask, flask, flask