code: def mojo_predict_pandas(
      dataframe,
      mojo_zip_path,
      genmodel_jar_path=None,
      classpath=None,
      java_options=None,
      verbose=False,
      setInvNumNA=False,
      predict_contributions=False,
      predict_calibrated=False,
  ):
      """
      MOJO scoring function to take a Pandas frame and use MOJO model as zip file to score.
  
      :param dataframe: Pandas frame to score.
      :param mojo_zip_path: Path to MOJO zip downloaded from H2O.
      :param genmodel_jar_path: Optional, path to genmodel jar file. If None (default) then the h2o-genmodel.jar in the same
          folder as the MOJO zip will be used.
      :param classpath: Optional, specifies custom user defined classpath which will be used when scoring. If None
          (default) then the default classpath for this MOJO model will be used.
      :param java_options: Optional, custom user defined options for Java. By default ``-Xmx4g`` is used.
      :param verbose: Optional, if True, then additional debug information will be printed. False by default.
      :param predict_contributions: if True, then return prediction contributions instead of regular predictions
          (only for tree-based models).
      :param predict_calibrated: if true, then return calibrated probabilities in addition to the predicted probabilities.
      :return: Pandas frame with predictions
      """
      tmp_dir = tempfile.mkdtemp()
      try:
          if not can_use_pandas():
              raise RuntimeError("Cannot import pandas")
          import pandas
  
          assert_is_type(dataframe, pandas.DataFrame)
          input_csv_path = os.path.join(tmp_dir, "input.csv")
          prediction_csv_path = os.path.join(tmp_dir, "prediction.csv")
          dataframe.to_csv(input_csv_path)
          mojo_predict_csv(
              input_csv_path=input_csv_path,
              mojo_zip_path=mojo_zip_path,
              output_csv_path=prediction_csv_path,
              genmodel_jar_path=genmodel_jar_path,
              classpath=classpath,
              java_options=java_options,
              verbose=verbose,
              setInvNumNA=setInvNumNA,
              predict_contributions=predict_contributions,
              predict_calibrated=predict_calibrated,
          )
          return pandas.read_csv(prediction_csv_path)
      finally:
          shutil.rmtree(tmp_dir)
  
imports: from .compatibility import *
  import csv
  import contextlib
  import io
  import itertools
  import os
  import re
  import shutil
  import string
  import subprocess
  import sys
  import tempfile
  import zipfile
  from h2o.backend.server import H2OLocalServer
  from h2o.exceptions import H2OValueError
  from h2o.utils.typechecks import assert_is_type, is_type, numeric
  from h2o.utils.threading import local_env
label_desc: Use image node_id 35 for calling a global function with example usage: mojo_predict_pandas(dataframe, mojo_zip_path, genmodel_jar_path, classpath, java_options, verbose, setInvNumNA, predict_contributions, predict_calibrated) and returns: pandas