docstr: Evaluates the model on the validation data set.
docstr_tok: ["Evaluates","the","model","on","the","validation","data","set","."]
code: def validate(
      self,
      data_creator,
      batch_size=32,
      verbose=1,
      sample_weight=None,
      steps=None,
      callbacks=None,
      data_config=None,
  ):
      """
      Evaluates the model on the validation data set.
      """
      config = copy.copy(self.config)
      if data_config is not None:
          config.update(data_config)
      config["batch_size"] = batch_size
  
      with self.strategy.scope():
          dataset_handler = DatasetHandler.get_handler(
              self.backend, self.rank, self.size
          )
  
          dataset = dataset_handler.handle_dataset_validation(
              data_creator, config=config, steps=steps
          )
      if callbacks:
          replaced_log_dir = process_tensorboard_in_callbacks(
              callbacks, "evaluate", self.rank
          )
  
      params = dict(
          verbose=verbose,
          sample_weight=sample_weight,
          steps=steps,
          callbacks=callbacks,
      )
      results = self.model.evaluate(dataset, **params)
  
      if results is None:
          if self.model_creator is not None:
              local_model = self.model_creator(self.config)
          else:
              local_model = tf.keras.models.load_model(
                  self.model_load, self.custom_objects, self.compile
              )
          if self.model_weights:
              local_model = local_model.set_weights(
                  self.model_weights.value
              )
          results = local_model.evaluate(dataset, **params)
  
      if isinstance(results, list):
          stats = {
              "validation_" + k: v
              for k, v in zip(self.model.metrics_names, results)
          }
      else:
          stats = {"validation_" + self.model.metrics_names[0]: results}
  
      # clean temporary dir for tensorboard
      if callbacks:
          if replaced_log_dir and os.path.exists(replaced_log_dir):
              shutil.rmtree(replaced_log_dir)
  
      if self.rank == 0:
          self._stop_log_monitor()
          return [stats]
      else:
          self._stop_log_monitor()
          return []
  
imports: import json
  import os
  import tempfile
  import shutil
  import copy
  import tensorflow
  from bigdl.orca.data.utils import partition_get_data_label
  from bigdl.orca.data.file import exists
  from bigdl.orca.learn.utils import save_pkl, duplicate_stdout_stderr_to_file, get_rank, process_tensorboard_in_callbacks, save_model, load_model, replace_specific_object_from_callbacks
  from bigdl.orca.learn.log_monitor import LogMonitor
  from bigdl.orca.learn.tf2.callbacks import ModelCheckpoint
  from bigdl.dllib.utils.log4Error import *
label_desc: Use image node_id 7 for calling the SparkRunner obj's underlying member method code with example usage: obj.validate(data_creator, batch_size, verbose, sample_weight, steps, callbacks, data_config) and returns: list, list