code: def evaluate(self, dataset, batch_size, val_methods):
      if isinstance(dataset, ImageSet):
          return callZooFunc(
              self.bigdl_type,
              "modelEvaluateImageFrame",
              self.value,
              dataset.to_image_frame(),
              batch_size,
              val_methods,
          )
  
      if isinstance(dataset, TFImageDataset):
          return callZooFunc(
              self.bigdl_type,
              "modelEvaluateImageFrame",
              self.value,
              dataset.get_evaluation_data(),
              batch_size,
              val_methods,
          )
      if isinstance(dataset, TFDataset):
          return callZooFunc(
              self.bigdl_type,
              "tfnetEvaluate",
              self.value,
              dataset.get_evaluation_data(),
              val_methods,
          )
      else:
          return callZooFunc(
              self.bigdl_type,
              "modelEvaluate",
              self.value,
              dataset,
              batch_size,
              val_methods,
          )
  
imports: import os
  import sys
  import tempfile
  import numpy
  import six
  from pyspark import RDD
  from bigdl.dllib.nn.layer import Layer
  from bigdl.dllib.utils.file_utils import JTensor
  from bigdl.dllib.nncontext import getOrCreateSparkContext
  from bigdl.dllib.utils.file_utils import callZooFunc
  from bigdl.dllib.feature.image import ImageSet
  from bigdl.orca.tfpark.tf_dataset import TFImageDataset, TFDataset
  import logging
  from bigdl.dllib.utils.utils import to_sample_rdd
  from bigdl.dllib.utils.log4Error import *
label_desc: Use image node_id 4 for calling the TFNet obj's underlying member method code with example usage: obj.evaluate(dataset, batch_size, val_methods) and returns: callZooFunc, callZooFunc, callZooFunc, callZooFunc