class_docstr: Manages a PyTorch model for training.
docstr: Evaluates the model on the validation data set.
docstr_tok: ["Evaluates","the","model","on","the","validation","data","set","."]
code: def predict(
      self, data_creator, batch_size=32, profile=False, callbacks=None
  ):
      """Evaluates the model on the validation data set."""
      config = copy.copy(self.config)
      self._toggle_profiling(profile=profile)
  
      shards_ref = data_creator(config, batch_size)
      if isinstance(shards_ref, IterableDataset):
          pred_stats = super().predict(
              partition=shards_ref,
              batch_size=batch_size,
              profile=profile,
              callbacks=callbacks,
          )
          for pred_stat in pred_stats:
              pred_stat.update(pred_stat)
          worker_stats = pred_stat["prediction"]
      else:
          if not isinstance(shards_ref, ray.ObjectID):
              invalidInputError(
                  False,
                  "Only xshards and Ray Dataset is supported for predict",
              )
          partition = ray.get(shards_ref)
          worker_stats = super().predict(
              partition=partition,
              batch_size=batch_size,
              profile=profile,
              callbacks=callbacks,
          )
      return worker_stats
  
file_path: BigDL/python/orca/src/bigdl/orca/learn/pytorch/pytorch_ray_worker.py
imports: import ray
  import copy
  from bigdl.orca.learn.pytorch.utils import find_free_port
  from bigdl.orca.learn.pytorch.torch_runner import TorchRunner
  import torch.nn
  from torch.utils.data import IterableDataset
  import logging
  from bigdl.dllib.utils.log4Error import *
label_desc: Use image node_id 6 for calling the PytorchRayWorker obj's underlying member method code with example usage: obj.predict(data_creator, batch_size, profile, callbacks) and returns: worker_stats