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.nn.layer import Layer
  from zoo.common import JTensor
  from zoo.common.nncontext import getOrCreateSparkContext
  from zoo.common.utils import callZooFunc
  from zoo.feature.image import ImageSet
  from zoo.tfpark.tf_dataset import TFImageDataset, TFDataset
  import logging
  from zoo.util.utils import to_sample_rdd
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