docstr: Evaluates the model on the validation data set.
  :param data: evaluate data. It can be XShards, Spark DataFrame or creator function which
         returns Iter or DataLoader.
         If data is XShards, each partition can be a Pandas DataFrame or a dictionary of
         {'x': feature, 'y': label}, where feature(label) is a numpy array or a tuple of
         numpy arrays.
  :param batch_size: Total batch size for all workers used for evaluation. Each worker's batch
         size would be this value divide the total number of workers. Default: 32.
  :param verbose: Prints output of one model if true.
  :param callbacks: List of Keras compatible callbacks to apply during evaluation.
  :return: validation result
docstr_tok: ["Evaluates","the","model","on","the","validation","data","set",".",":","param","data",":","evaluate","data",".","It","can","be","XShards",",","Spark","DataFrame","or","creator","function","which","returns","Iter","or","DataLoader",".","If","data","is","XShards",",","each","partition","can","be","a","Pandas","DataFrame","or","a","dictionary","of","{","'","x","'",":","feature",",","'","y","'",":","label","}",",","where","feature","(","label",")","is","a","numpy","array","or","a","tuple","of","numpy","arrays",".",":","param","batch_size",":","Total","batch","size","for","all","workers","used","for","evaluation",".","Each","worker","'s","batch","size","would","be","this","value","divide","the","total","number","of","workers",".","Default",":","32.",":","param","verbose",":","Prints","output","of","one","model","if","true",".",":","param","callbacks",":","List","of","Keras","compatible","callbacks","to","apply","during","evaluation",".",":","return",":","validation","result"]
code: def evaluate(
      self,
      data: Union["SparkXShards", "SparkDataFrame", Callable],
      batch_size: int = 32,
      num_steps: Optional[int] = None,
      verbose: Union[str, int] = 1,
      sample_weight: Optional["np.ndarray"] = None,
      callbacks: Optional[List["Callback"]] = None,
      data_config: Optional[Dict] = None,
      feature_cols: Optional[List[str]] = None,
      label_cols: Optional[List[str]] = None,
  ) -> Dict:
      """
      Evaluates the model on the validation data set.
      :param data: evaluate data. It can be XShards, Spark DataFrame or creator function which
             returns Iter or DataLoader.
             If data is XShards, each partition can be a Pandas DataFrame or a dictionary of
             {'x': feature, 'y': label}, where feature(label) is a numpy array or a tuple of
             numpy arrays.
      :param batch_size: Total batch size for all workers used for evaluation. Each worker's batch
             size would be this value divide the total number of workers. Default: 32.
      :param verbose: Prints output of one model if true.
      :param callbacks: List of Keras compatible callbacks to apply during evaluation.
      :return: validation result
      """
      if not isinstance(data, types.FunctionType):
          invalidInputError(
              isinstance(batch_size, int) and batch_size > 0,
              "batch_size should be a positive integer",
          )
      else:
          # batch_size can be None if the return of data_creator already generates batches
          if batch_size:
              invalidInputError(
                  isinstance(batch_size, int) and batch_size > 0,
                  "batch_size should be a positive integer",
              )
      # Use the local batch size for each worker to convert to XShards
      if batch_size:
          local_batch_size = batch_size // self.num_workers
          if local_batch_size <= 0:
              local_batch_size = 1
      else:
          local_batch_size = None
      sc = OrcaContext.get_spark_context()
      logger.info("Starting validation step.")
  
      if isinstance(data, SparkXShards):
          data = data.to_lazy()
      if isinstance(data, DataFrame) or isinstance(data, SparkXShards):
          if data.rdd.getNumPartitions() != self.num_workers:
              data = data.repartition(self.num_workers)
      data, _ = maybe_dataframe_to_xshards(
          data,
          validation_data=None,
          feature_cols=feature_cols,
          label_cols=label_cols,
          mode="evaluate",
          num_workers=self.num_workers,
          accept_str_col=True,
          shard_size=local_batch_size,
      )
  
      if self.model_weights:
          weights = sc.broadcast(self.model_weights)
      else:
          weights = None
  
      init_params = dict(
          model_creator=self.model_creator,
          model_load=self.load_path,
          compile_args_creator=self.compile_args_creator,
          config=self.config,
          verbose=self.verbose,
          size=self.num_workers,
          model_weights=weights,
          mode="evaluate",
          cluster_info=self._get_cluster_info(sc),
          model_dir=self.model_dir,
          application_id=self.application_id,
          need_to_log_to_driver=self.need_to_log_to_driver,
          driver_ip=self.ip,
          driver_port=self.port,
      )
  
      if self.load_params is not None:
          init_params.update(self.load_params)
  
      params = dict(
          batch_size=batch_size,
          verbose=verbose,
          sample_weight=sample_weight,
          steps=num_steps,
          callbacks=callbacks,
          data_config=data_config,
      )
  
      if isinstance(
          data, SparkXShards
      ):  # Computation triggered when collect
          if data._get_class_name() == "pandas.core.frame.DataFrame":
              data = process_xshards_of_pandas_dataframe(
                  data, feature_cols, label_cols
              )
  
          def transform_func(iter, init_param, param):
              partition_data = list(iter)
              param["data_creator"] = make_data_creator(partition_data)
              return SparkRunner(**init_param).validate(**param)
  
          res = (
              data.rdd.barrier()
              .mapPartitions(  # type:ignore
                  lambda iter: transform_func(iter, init_params, params)
              )
              .collect()
          )
      else:
          params["data_creator"] = data  # type:ignore
  
          def transform_func(iter, init_param, param):
              return SparkRunner(**init_param).validate(**param)
  
          res = (
              self.workerRDD.barrier()
              .mapPartitions(
                  lambda iter: transform_func(iter, init_params, params)
              )
              .collect()
          )
      return res[0]
  
imports: import os
  import types
  import logging
  import tempfile
  import shutil
  from bigdl.dllib.utils.common import get_node_and_core_number
  from bigdl.dllib.utils.file_utils import is_local_path
  from bigdl.dllib.utils.utils import get_node_ip
  from bigdl.orca.data.file import is_file, exists, get_remote_file_to_local, get_remote_files_with_prefix_to_local, put_local_file_to_remote, put_local_files_with_prefix_to_remote
  from bigdl.orca.learn.tf2.spark_runner import SparkRunner
  from bigdl.orca.learn.utils import find_free_port, find_ip_and_free_port
  from bigdl.orca.learn.utils import maybe_dataframe_to_xshards, dataframe_to_xshards, convert_predict_xshards_to_dataframe, make_data_creator, load_model, save_model, process_xshards_of_pandas_dataframe, add_predict_to_pd_xshards, update_predict_xshards
  from bigdl.orca.learn.log_monitor import start_log_server, stop_log_server
  from bigdl.orca import OrcaContext
  from bigdl.orca.data.shard import SparkXShards
  from bigdl.dllib.utils.log4Error import invalidInputError
  from pyspark.sql.dataframe import DataFrame
  from typing import TYPE_CHECKING, Any, Dict, List, Callable, Union, Optional
label_desc: Use image node_id 4 for calling the SparkTFEstimator obj's underlying member method code with example usage: obj.evaluate(data, batch_size, num_steps, verbose, sample_weight, callbacks, data_config, feature_cols, label_cols) and returns: res, SparkRunner, SparkRunner