class_docstr: Abstraction for using a EvalSavedModel.
  
  Note that this class overrides eval_metrics_graph.EvalMetricsGraph
  which contains most of the functionality for handling the metric ops.
  In the eval saved model path, a common graph is shared between generation
  FeaturesPredictionLabels through Predict and doing the metric ops.
  The specific methods of this class constructs the graph and handles
  the prediction ops.
docstr: Gets features, predictions, labels as FeaturesPredictionsLabelsType.
docstr_tok: ["Gets","features",",","predictions",",","labels","as","FeaturesPredictionsLabelsType","."]
code: def as_features_predictions_labels(
      self, fetched_values: List[FetchedTensorValues]
  ) -> List[types.FeaturesPredictionsLabels]:
      """Gets features, predictions, labels as FeaturesPredictionsLabelsType."""
  
      def fpl_dict(
          fetched: FetchedTensorValues, group: str
      ) -> types.DictOfFetchedTensorValues:
          native = fetched.values[group]
          wrapped = {}
          if not isinstance(native, dict):
              native = {util.default_dict_key(group): native}
          for key in native:
              wrapped[key] = {encoding.NODE_SUFFIX: native[key]}
          return wrapped
  
      fpls = []
      for fetched in fetched_values:
          fpls.append(
              types.FeaturesPredictionsLabels(
                  input_ref=fetched.input_ref,
                  features=fpl_dict(fetched, constants.FEATURES_NAME),
                  predictions=fpl_dict(
                      fetched, constants.PREDICTIONS_NAME
                  ),
                  labels=fpl_dict(fetched, constants.LABELS_NAME),
              )
          )
      return fpls
  
imports: from typing import Any, Dict, Generator, List, NamedTuple, Optional, Tuple, Union
  import numpy
  import tensorflow
  from tensorflow_model_analysis.api import types
  from tensorflow_model_analysis.eval_metrics_graph import eval_metrics_graph
  from tensorflow_model_analysis.eval_saved_model import constants
  from tensorflow_model_analysis.eval_saved_model import encoding
  from tensorflow_model_analysis.eval_saved_model import graph_ref
  from tensorflow_model_analysis.eval_saved_model import util
  from tensorflow.core.protobuf import meta_graph_pb2
label_desc: Use image node_id 11 for calling the EvalSavedModel obj's underlying member method code with example usage: obj.as_features_predictions_labels(fetched_values) and returns: fpls, wrapped