code: def _evaluate(self, rdd: RDD, **kwargs):
      json_model = self.master_network.to_json()
      optimizer = deserialize_optimizer(self.master_optimizer)
      loss = self.master_loss
      weights = self.master_network.get_weights()
      weights = rdd.context.broadcast(weights)
      custom_objects = self.custom_objects
      metrics = self.master_metrics
  
      def _evaluate(
          model,
          optimizer,
          loss,
          custom_objects,
          metrics,
          kwargs,
          data_iterator,
      ):
          model = model_from_json(model, custom_objects)
          model.compile(optimizer, loss, metrics)
          model.set_weights(weights.value)
          feature_iterator, label_iterator = tee(data_iterator, 2)
          x_test = np.asarray([x for x, y in feature_iterator])
          y_test = np.asarray([y for x, y in label_iterator])
          return [model.evaluate(x_test, y_test, **kwargs)]
  
      if self.num_workers:
          rdd = rdd.repartition(self.num_workers)
      results = rdd.mapPartitions(
          partial(
              _evaluate,
              json_model,
              optimizer,
              loss,
              custom_objects,
              metrics,
              kwargs,
          )
      )
      if not metrics:
          # if no metrics, we can just return the scalar corresponding to the loss value
          return results.mean()
      else:
          # if we do have metrics, we want to return a list of [loss value, metric value] - to match the keras API
          loss_value = results.map(lambda x: x[0]).mean()
          metric_value = results.map(lambda x: x[1]).mean()
          return [loss_value, metric_value]
  
imports: import json
  from functools import partial
  from itertools import tee
  from typing import Union
  import h5py
  import numpy
  import pyspark
  from pyspark import RDD
  from tensorflow.keras.models import load_model
  from tensorflow.keras.models import model_from_json
  from tensorflow.keras.optimizers import get
  from tensorflow.keras.optimizers import serialize, deserialize
  from .mllib import to_matrix, from_matrix, to_vector, from_vector
  from .parameter.factory import ClientServerFactory
  from .utils import lp_to_simple_rdd, to_simple_rdd
  from .utils import model_to_dict
  from .utils import subtract_params, divide_by
  from .worker import AsynchronousSparkWorker, SparkWorker
label_desc: Use image node_id 14 for calling the SparkModel obj's underlying member method code with example usage: obj._evaluate(rdd) and returns: list, results, list