docstr: Evaluate model.
  
  :param data: evaluation data.
  :param batch_size: batch size per thread.
  :param num_steps: Number of batches to compute update steps on. This corresponds also to
  the number of times TorchRunner.validate_batch is called.
  :return: evaluation result as a dictionary of {'metric name': metric value}
docstr_tok: ["Evaluate","model",".",":","param","data",":","evaluation","data",".",":","param","batch_size",":","batch","size","per","thread",".",":","param","num_steps",":","Number","of","batches","to","compute","update","steps","on",".","This","corresponds","also","to","the","number","of","times","TorchRunner.validate_batch","is","called",".",":","return",":","evaluation","result","as","a","dictionary","of","{","'metric","name","'",":","metric","value","}"]
code: def evaluate(self, data, batch_size, num_steps=None):
      """
      Evaluate model.
  
      :param data: evaluation data.
      :param batch_size: batch size per thread.
      :param num_steps: Number of batches to compute update steps on. This corresponds also to
      the number of times TorchRunner.validate_batch is called.
      :return: evaluation result as a dictionary of {'metric name': metric value}
      """
      pass
  
file_path: BigDL/python/orca/src/bigdl/orca/learn/ray_estimator.py
imports: from abc import abstractmethod
  from bigdl.orca.learn.base_estimator import BaseEstimator
label_desc: Use image node_id 3 for calling the Estimator obj's underlying member method code with example usage: obj.evaluate(data, batch_size, num_steps) without return types