code: def generate_spark_df():
      sc = OrcaContext.get_spark_context()
      from pyspark.sql import SparkSession
  
      spark = SparkSession(sc)
      rdd = sc.range(0, 100)
      from pyspark.ml.linalg import DenseVector
  
      df = rdd.map(
          lambda x: (
              DenseVector(
                  np.random.randn(
                      1,
                  ).astype(np.float32)
              ),
              int(np.random.randint(0, 2, size=())),
              int(x),
          )
      ).toDF(["feature", "id", "date"])
      return df
  
imports: import pytest
  import numpy
  import pandas
  import random
  import os
  from unittest import TestCase
  from bigdl.chronos.data import TSDataset
  from bigdl.chronos.utils import LazyImport
  from ...None import op_torch, op_distributed
  from pandas.testing import assert_frame_equal
  from numpy.testing import assert_array_almost_equal
label_desc: Use image node_id 1 for calling a global function with example usage: generate_spark_df() and returns: df