docstr: Evaluates the model on the validation data set.
  
  :param data: evaluate data. It can be XShards, Spark DataFrame, Ray Dataset 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 num_steps: Total number of steps (batches of samples) before declaring the evaluation
         round finished. Ignored with the default value of `None`.
  :param verbose: Prints output of one model if true.
  :param sample_weight: Optional Numpy array of weights for the training samples, used for
         weighting the loss function. You can either pass a flat (1D) Numpy array with the
         same length as the input samples (1:1 mapping between weights and samples), or in
         the case of temporal data, you can pass a 2D array with shape (samples,
         sequence_length), to apply a different weight to every timestep of every sample.
  :param callbacks: List of Keras compatible callbacks to apply during evaluation.
  :param data_config: An optional dictionary that can be passed to data creator function.
         If data is a Ray Dataset, specifies `output_signature` same as in
         `tf.data.Dataset.from_generator` (If `label_cols` is specified, a 2-element
         tuple of `tf.TypeSpec` objects corresponding to (features, label). Otherwise,
         a single `tf.TypeSpec` corresponding to features tensor).
  :param feature_cols: Feature column name(s) of data. Only used when data is a Spark
         DataFrame, an XShards of Pandas DataFrame or a Ray Dataset. Default: None.
  :param label_cols: Label column name(s) of data. Only used when data is a Spark DataFrame,
         an XShards of Pandas DataFrame or a Ray Dataset.
         Default: None.
  :return: validation result
code: def evaluate(
      self,
      data: Union[
          "SparkXShards",
          "SparkDataFrame",
          "TFDataset",
          "ray.data.Dataset",
          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, Ray Dataset 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 num_steps: Total number of steps (batches of samples) before declaring the evaluation
             round finished. Ignored with the default value of `None`.
      :param verbose: Prints output of one model if true.
      :param sample_weight: Optional Numpy array of weights for the training samples, used for
             weighting the loss function. You can either pass a flat (1D) Numpy array with the
             same length as the input samples (1:1 mapping between weights and samples), or in
             the case of temporal data, you can pass a 2D array with shape (samples,
             sequence_length), to apply a different weight to every timestep of every sample.
      :param callbacks: List of Keras compatible callbacks to apply during evaluation.
      :param data_config: An optional dictionary that can be passed to data creator function.
             If data is a Ray Dataset, specifies `output_signature` same as in
             `tf.data.Dataset.from_generator` (If `label_cols` is specified, a 2-element
             tuple of `tf.TypeSpec` objects corresponding to (features, label). Otherwise,
             a single `tf.TypeSpec` corresponding to features tensor).
      :param feature_cols: Feature column name(s) of data. Only used when data is a Spark
             DataFrame, an XShards of Pandas DataFrame or a Ray Dataset. Default: None.
      :param label_cols: Label column name(s) of data. Only used when data is a Spark DataFrame,
             an XShards of Pandas DataFrame or a Ray Dataset.
             Default: None.
      :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
      logger.info("Starting validation step.")
      params = dict(
          batch_size=batch_size,
          verbose=verbose,
          sample_weight=sample_weight,
          steps=num_steps,
          callbacks=callbacks,
          data_config=data_config,
      )
      from bigdl.orca.data import SparkXShards
      from bigdl.orca.data.tf.data import Dataset
      from bigdl.orca.data.tf.tf2_data import TF2Dataset
  
      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 isinstance(data, SparkXShards):
          # Make sure each worker can get at least one data partition
          if data.num_partitions() < self.num_workers:
              data = data.repartition(self.num_workers)
          if data._get_class_name() == "pandas.core.frame.DataFrame":
              data = process_xshards_of_pandas_dataframe(
                  data, feature_cols, label_cols
              )
          ray_xshards = RayXShards.from_spark_xshards(
              data
          )  # type:ignore
          worker_stats = self._evaluate_ray_xshards(ray_xshards, params)
      elif isinstance(data, Dataset):
          ray_xshards = TF2Dataset(data).get_ray_xshards(
              self.num_workers
          )
          worker_stats = self._evaluate_ray_xshards(ray_xshards, params)
      elif isinstance(data, ray.data.Dataset):
          shards = data.split(
              n=self.num_workers, locality_hints=self.remote_workers
          )
  
          remote_worker_stats = []
          for shard, worker in zip(shards, self.remote_workers):
              params["data_creator"] = self.process_ray_dataset(
                  shard, label_cols, feature_cols, data_config
              )
              remote_worker_stats.append(
                  worker.validate.remote(**params)
              )
          worker_stats = ray.get(remote_worker_stats)
      else:  # data_creator functions; should return Iter or DataLoader
          params["data_creator"] = data  # type:ignore
          params_list = [params] * self.num_workers
  
          worker_stats = ray.get(
              [
                  w.validate.remote(**params_list[i])
                  for i, w in enumerate(self.remote_workers)
              ]
          )
      stats = worker_stats[0].copy()
      return stats
  
imports: import os
  import types
  from bigdl.orca.common import SafePickle
  import shutil
  import tempfile
  import logging
  import ray
  from bigdl.dllib.utils import log4Error
  from bigdl.dllib.utils.file_utils import is_local_path
  from bigdl.orca.data.file import enable_multi_fs_save, enable_multi_fs_load
  from bigdl.orca.data.ray_xshards import RayXShards
  from bigdl.orca.learn.dl_cluster import RayDLCluster
  from bigdl.orca.learn.tf2.tf_runner import TFRunner
  from bigdl.orca.learn.ray_estimator import Estimator
  from bigdl.orca.learn.utils import maybe_dataframe_to_xshards, dataframe_to_xshards, convert_predict_xshards_to_dataframe, update_predict_xshards, process_xshards_of_pandas_dataframe, make_data_creator, add_predict_to_pd_xshards
  from bigdl.orca.data.file import get_remote_file_to_local, get_remote_dir_to_local, is_file
  from bigdl.orca.data.utils import process_spark_xshards
  from bigdl.dllib.utils.log4Error import invalidInputError
  from bigdl.orca.ray import OrcaRayContext
  from typing import TYPE_CHECKING, Dict, List, Callable, Union, Optional
label_desc: Use image node_id 4 for calling the TensorFlow2Estimator 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: stats