docstr: Get the scalar from model validation summary.
  
  This method will return a list of summary data of
  [iteration_number, scalar_value, timestamp].
  Note that the metric and tag may not be consistent.
  Please look up following form to pass tag parameter.
  Left side is your metric during compile.
  Right side is the tag you should pass.
  
  >>> 'Accuracy'                  |   'Top1Accuracy'
  >>> 'BinaryAccuracy'            |   'Top1Accuracy'
  >>> 'CategoricalAccuracy'       |   'Top1Accuracy'
  >>> 'SparseCategoricalAccuracy' |   'Top1Accuracy'
  >>> 'AUC'                       |   'AucScore'
  >>> 'HitRatio'                  |   'HitRate@k' (k is Top-k)
  >>> 'Loss'                      |   'Loss'
  >>> 'MAE'                       |   'MAE'
  >>> 'NDCG'                      |   'NDCG'
  >>> 'TFValidationMethod'        |   '${name + " " + valMethod.toString()}'
  >>> 'Top5Accuracy'              |   'Top5Accuracy'
  >>> 'TreeNNAccuracy'            |   'TreeNNAccuracy()'
  >>> 'MeanAveragePrecision'      |   'MAP@k' (k is Top-k) (BigDL)
  >>> 'MeanAveragePrecision'      |   'PascalMeanAveragePrecision' (Zoo)
  >>> 'StatelessMetric'           |   '${name}'
  
  :param tag: The string variable represents the scalar wanted
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code: def get_validation_summary(self, tag=None):
      """
      Get the scalar from model validation summary.
  
      This method will return a list of summary data of
      [iteration_number, scalar_value, timestamp].
      Note that the metric and tag may not be consistent.
      Please look up following form to pass tag parameter.
      Left side is your metric during compile.
      Right side is the tag you should pass.
  
      >>> 'Accuracy'                  |   'Top1Accuracy'
      >>> 'BinaryAccuracy'            |   'Top1Accuracy'
      >>> 'CategoricalAccuracy'       |   'Top1Accuracy'
      >>> 'SparseCategoricalAccuracy' |   'Top1Accuracy'
      >>> 'AUC'                       |   'AucScore'
      >>> 'HitRatio'                  |   'HitRate@k' (k is Top-k)
      >>> 'Loss'                      |   'Loss'
      >>> 'MAE'                       |   'MAE'
      >>> 'NDCG'                      |   'NDCG'
      >>> 'TFValidationMethod'        |   '${name + " " + valMethod.toString()}'
      >>> 'Top5Accuracy'              |   'Top5Accuracy'
      >>> 'TreeNNAccuracy'            |   'TreeNNAccuracy()'
      >>> 'MeanAveragePrecision'      |   'MAP@k' (k is Top-k) (BigDL)
      >>> 'MeanAveragePrecision'      |   'PascalMeanAveragePrecision' (Zoo)
      >>> 'StatelessMetric'           |   '${name}'
  
      :param tag: The string variable represents the scalar wanted
      """
      if self.is_nnframe_fit:
          invalidInputError(
              tag is not None,
              "You should provide tag which should match the name of "
              "the ValidationMethod set into the optimizer. "
              "e.g.'MAE', 'Top1AccuracyLoss', 'Top1Accuracy' or "
              "'Top5Accuracy'.",
          )
          val_summary = self.nn_estimator.getValidationSummary()
          return val_summary.read_scalar(tag=tag)
      else:
          return self.estimator.get_validation_summary(tag=tag)
  
imports: from bigdl.orca.learn.metrics import Metric
  from bigdl.orca.learn.utils import bigdl_metric_results_to_dict
  from bigdl.dllib.nnframes import NNEstimator, NNModel
  from bigdl.dllib.estimator import Estimator
  from bigdl.orca.learn.spark_estimator import Estimator
  from bigdl.orca.data import SparkXShards
  from bigdl.dllib.optim.optimizer import MaxEpoch
  from bigdl.dllib.feature.common import FeatureSet
  from bigdl.orca.learn.metrics import Accuracy
  from pyspark.sql.dataframe import DataFrame
  from bigdl.dllib.utils.log4Error import *
label_desc: Use image node_id 14 for calling the BigDLEstimator obj's underlying member method code with example usage: obj.get_validation_summary(tag) and returns: val_summary, self