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: Initializes EvalSavedModel.
  
  Args:
    path: Path to model.
    include_default_metrics: True to include the in-graph metrics by default.
    additional_fetches: Prefixes of additional tensors stored in
      signature_def.inputs that should be fetched at prediction time. The
      "features" and "labels" tensors are handled automatically and should not
      be included in this list.
    blacklist_feature_fetches: List of tensor names in the features dictionary
      which should be excluded from the fetches request. This is useful in
      scenarios where features are large (e.g. images) and can lead to
      excessive memory use if stored.
    tags: Tags to use when loading the saved model.
  
  Raises:
    ValueError: If "features" or "labels" included in additional_fetches.
docstr_tok: ["Initializes","EvalSavedModel",".","Args",":","path",":","Path","to","model",".","include_default_metrics",":","True","to","include","the","in-graph","metrics","by","default",".","additional_fetches",":","Prefixes","of","additional","tensors","stored","in","signature_def.inputs","that","should","be","fetched","at","prediction","time",".","The","``","features","''","and","``","labels","''","tensors","are","handled","automatically","and","should","not","be","included","in","this","list",".","blacklist_feature_fetches",":","List","of","tensor","names","in","the","features","dictionary","which","should","be","excluded","from","the","fetches","request",".","This","is","useful","in","scenarios","where","features","are","large","(","e.g",".","images",")","and","can","lead","to","excessive","memory","use","if","stored",".","tags",":","Tags","to","use","when","loading","the","saved","model",".","Raises",":","ValueError",":","If","``","features","''","or","``","labels","''","included","in","additional_fetches","."]
code: def __init__(
      self,
      path: str,
      include_default_metrics: Optional[bool] = True,
      additional_fetches: Optional[List[str]] = None,
      blacklist_feature_fetches: Optional[List[str]] = None,
      tags: Optional[List[str]] = None,
  ):
      """Initializes EvalSavedModel.
  
      Args:
        path: Path to model.
        include_default_metrics: True to include the in-graph metrics by default.
        additional_fetches: Prefixes of additional tensors stored in
          signature_def.inputs that should be fetched at prediction time. The
          "features" and "labels" tensors are handled automatically and should not
          be included in this list.
        blacklist_feature_fetches: List of tensor names in the features dictionary
          which should be excluded from the fetches request. This is useful in
          scenarios where features are large (e.g. images) and can lead to
          excessive memory use if stored.
        tags: Tags to use when loading the saved model.
  
      Raises:
        ValueError: If "features" or "labels" included in additional_fetches.
      """
      self._path = path
      self._include_default_metrics = include_default_metrics
      if additional_fetches:
          if constants.FEATURES_NAME in additional_fetches:
              raise ValueError(
                  'additional_fetches should not contain "features"'
              )
          if constants.LABELS_NAME in additional_fetches:
              raise ValueError(
                  'additional_fetches should not contain "labels"'
              )
      self._additional_fetches = additional_fetches
      self._blacklist_feature_fetches = blacklist_feature_fetches
      if tags:
          self._tags = tags
      else:
          self._tags = [constants.EVAL_TAG]
      super().__init__()
  
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 1 to create a new EvalSavedModel object from inherited base classes: eval_metrics_graph with example: obj = EvalSavedModel(path, include_default_metrics, additional_fetches, blacklist_feature_fetches, tags)