parameters: (self, features, targets, metrics)
docstring: Method that builds model graph and returns evaluation ops.
  
      Expected to be overriden by sub-classes that require custom support.
  
      Args:
        features: `Tensor` or `dict` of `Tensor` objects.
        targets: `Tensor` or `dict` of `Tensor` objects.
        metrics: Dict of metric ops to run. If None, the default metric functions
          are used; if {}, no metrics are used. If model has one output (i.e.,
          returning single predction), keys are `str`, e.g. `'accuracy'` - just a
          name of the metric that will show up in the logs / summaries.
          Otherwise, keys are tuple of two `str`, e.g. `('accuracy', 'classes')`
          - name of the metric and name of `Tensor` in the predictions to run
          this metric on. Metric ops should support streaming, e.g., returning
          update_op and value tensors. See more details in
          ../../../../metrics/python/metrics/ops/streaming_metrics.py.
  
      Returns:
        metrics: `dict` of `Tensor` objects.
docstring_summary: Method that builds model graph and returns evaluation ops.
docstring_tokens: Method that builds model graph and returns evaluation ops .
function: def _get_eval_ops(self, features, targets, metrics):
      """Method that builds model graph and returns evaluation ops.
  
      Expected to be overriden by sub-classes that require custom support.
  
      Args:
        features: `Tensor` or `dict` of `Tensor` objects.
        targets: `Tensor` or `dict` of `Tensor` objects.
        metrics: Dict of metric ops to run. If None, the default metric functions
          are used; if {}, no metrics are used. If model has one output (i.e.,
          returning single predction), keys are `str`, e.g. `'accuracy'` - just a
          name of the metric that will show up in the logs / summaries.
          Otherwise, keys are tuple of two `str`, e.g. `('accuracy', 'classes')`
          - name of the metric and name of `Tensor` in the predictions to run
          this metric on. Metric ops should support streaming, e.g., returning
          update_op and value tensors. See more details in
          ../../../../metrics/python/metrics/ops/streaming_metrics.py.
  
      Returns:
        metrics: `dict` of `Tensor` objects.
      """
      raise NotImplementedError('_get_eval_ops not implemented in BaseEstimator')
function_tokens: def _get_eval_ops ( self , features , targets , metrics ) : raise NotImplementedError ( '_get_eval_ops not implemented in BaseEstimator' )