code: def predict(self, x, batchsize=4, **kargs):
      i = 0
      result = []
      while i + batchsize < len(x):
          x_batch = x[i : i + batchsize]
          x_batch, _ = convert_to_jtensor(x_batch, **kargs)
          result_batch = callBigDlFunc(
              self.bigdl_type, "fgBoostPredict", self.value, x_batch
          ).to_ndarray()
          result.append(result_batch.flatten())
          i += batchsize
      x_batch = x[i:]
      x_batch, _ = convert_to_jtensor(x_batch, **kargs)
      result_batch = callBigDlFunc(
          self.bigdl_type, "fgBoostPredict", self.value, x_batch
      ).to_ndarray()
      result.append(result_batch.flatten())
      flat_result = [x for xs in result for x in xs]
      return np.array(flat_result)
  
file_path: BigDL/python/ppml/src/bigdl/ppml/fl/algorithms/fgboost_regression.py
imports: from time import time
  from bigdl.dllib.utils.common import JavaValue
  from bigdl.ppml.fl.data_utils import *
  from bigdl.ppml.fl import *
  from bigdl.ppml.fl.fgboost.utils import add_data
  import logging
label_desc: Use image node_id 4 for calling the FGBoostRegression obj's underlying member method code with example usage: obj.predict(x, batchsize) and returns: np