code: def create_pandas_data_frame(cls):
      data_dict = {}
      for j, name in enumerate(cls.data_type.names):
          data_dict[name] = [
              cls.data[i][j] for i in range(len(cls.data))
          ]
      # need convert to numpy types
      import numpy as np
  
      data_dict["f1"] = np.int8(data_dict["f1"])
      data_dict["f2"] = np.int16(data_dict["f2"])
      data_dict["f3"] = np.int32(data_dict["f3"])
      data_dict["f4"] = np.int64(data_dict["f4"])
      data_dict["f6"] = np.float32(data_dict["f6"])
      data_dict["f7"] = np.float64(data_dict["f7"])
      data_dict["f15"] = [row.as_dict() for row in data_dict["f15"]]
      import pandas as pd
  
      return pd.DataFrame(
          data=data_dict,
          index=[2.0, 3.0],
          columns=[
              "f1",
              "f2",
              "f3",
              "f4",
              "f5",
              "f6",
              "f7",
              "f8",
              "f9",
              "f10",
              "f11",
              "f12",
              "f13",
              "f14",
              "f15",
          ],
      )
  
imports: import datetime
  import decimal
  from pandas.testing import assert_frame_equal
  from pyflink.common import Row
  from pyflink.table.types import DataTypes
  from pyflink.testing import source_sink_utils
  from pyflink.testing.test_case_utils import PyFlinkBatchTableTestCase, PyFlinkStreamTableTestCase
label_desc: Use image node_id 2 for calling the PandasConversionTestBase obj's underlying member method code with example usage: obj.create_pandas_data_frame(cls) and returns: pd