docstr: Train and evaluate the estimator.
  
  :param train_spec: A TrainSpec instance to specify the training specification.
  :param eval_spec: A EvalSpec instance to specify the evaluation and export specification.
  
  Returns:
      A tuple of the result of the evaluate call to the Estimator and the export results using
       the specified ExportStrategy. Currently, the return value is undefined for distributed
      training mode.
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code: def train_and_evaluate(
      self, train_spec: "TrainSpec", eval_spec: "EvalSpec"
  ) -> Any:
      """
      Train and evaluate the estimator.
  
      :param train_spec: A TrainSpec instance to specify the training specification.
      :param eval_spec: A EvalSpec instance to specify the evaluation and export specification.
  
      Returns:
          A tuple of the result of the evaluate call to the Estimator and the export results using
           the specified ExportStrategy. Currently, the return value is undefined for distributed
          training mode.
      """
      params = dict(
          train_spec=train_spec,
          eval_spec=eval_spec,
      )
      results = ray.get(
          [
              worker.train_and_evaluate.remote(**params)
              for worker in self.remote_workers
          ]
      )
      return results
  
file_path: BigDL/python/orca/src/bigdl/orca/learn/tf/tf_estimator.py
imports: import ray
  from bigdl.orca.ray import OrcaRayContext
  from bigdl.orca.learn.tf.tf_runner import TFRunner
  from bigdl.orca.learn.dl_cluster import RayDLCluster
  from typing import TYPE_CHECKING, Any, Dict, Optional, Callable
label_desc: Use image node_id 2 for calling the TFEstimator obj's underlying member method code with example usage: obj.train_and_evaluate(train_spec, eval_spec) and returns: results