def default_eval_shared_model(eval_saved_model_path: Text, add_metrics_callbacks: Optional[List[types.AddMetricsCallbackType]]=None, include_default_metrics: Optional[bool]=True, example_weight_key: Optional[Union[(Text, Dict[(Text, Text)])]]=None, additional_fetches: Optional[List[Text]]=None, blacklist_feature_fetches: Optional[List[Text]]=None, tags: Optional[List[Text]]=None, eval_config: Optional[config.EvalConfig]=None) -> types.EvalSharedModel:
    'Returns default EvalSharedModel.\n\n  Args:\n    eval_saved_model_path: Path to EvalSavedModel.\n    add_metrics_callbacks: Optional list of callbacks for adding additional\n      metrics to the graph (see EvalSharedModel for more information on how to\n      configure additional metrics). Metrics for example count and example\n      weights will be added automatically.\n    include_default_metrics: True to include the default metrics that are part\n      of the saved model graph during evaluation. Note that\n      eval_config.options.include_default_metrics must also be true.\n    example_weight_key: Example weight key (single-output model) or dict of\n      example weight keys (multi-output model) keyed by output name.\n    additional_fetches: Prefixes of additional tensors stored in\n      signature_def.inputs that should be fetched at prediction time. The\n      "features" and "labels" tensors are handled automatically and should not\n      be included.\n    blacklist_feature_fetches: List of tensor names in the features dictionary\n      which should be excluded from the fetches request. This is useful in\n      scenarios where features are large (e.g. images) and can lead to excessive\n      memory use if stored.\n    tags: Model tags (e.g. \'serve\' for serving or \'eval\' for EvalSavedModel).\n    eval_config: Eval config. Only used for setting default tags.\n  '
    if (tags is None):
        if eval_config:
            signatures = [s.signature_name for s in eval_config.model_specs]
            if (eval_constants.EVAL_TAG in signatures):
                if (not all(((s == eval_constants.EVAL_TAG) for s in signatures))):
                    tf.compat.v1.logging.warning('mixture of eval and non-eval signatures used: eval_config={}'.format(eval_config))
                tags = [eval_constants.EVAL_TAG]
            else:
                tags = [tf.saved_model.SERVING]
        else:
            tags = [eval_constants.EVAL_TAG]
    if (tags == [eval_constants.EVAL_TAG]):
        if (not add_metrics_callbacks):
            add_metrics_callbacks = []
        example_count_callback = post_export_metrics.example_count()
        add_metrics_callbacks.append(example_count_callback)
        if example_weight_key:
            if isinstance(example_weight_key, dict):
                for (output_name, key) in example_weight_key.items():
                    example_weight_callback = post_export_metrics.example_weight(key, metric_tag=output_name)
                    add_metrics_callbacks.append(example_weight_callback)
            else:
                example_weight_callback = post_export_metrics.example_weight(example_weight_key)
                add_metrics_callbacks.append(example_weight_callback)
    return types.EvalSharedModel(model_path=eval_saved_model_path, add_metrics_callbacks=add_metrics_callbacks, include_default_metrics=include_default_metrics, example_weight_key=example_weight_key, additional_fetches=additional_fetches, model_loader=types.ModelLoader(tags=tags, construct_fn=model_util.model_construct_fn(eval_saved_model_path=eval_saved_model_path, add_metrics_callbacks=add_metrics_callbacks, include_default_metrics=include_default_metrics, additional_fetches=additional_fetches, blacklist_feature_fetches=blacklist_feature_fetches, tags=tags)))
Returns default EvalSharedModel.

Args:
  eval_saved_model_path: Path to EvalSavedModel.
  add_metrics_callbacks: Optional list of callbacks for adding additional
    metrics to the graph (see EvalSharedModel for more information on how to
    configure additional metrics). Metrics for example count and example
    weights will be added automatically.
  include_default_metrics: True to include the default metrics that are part
    of the saved model graph during evaluation. Note that
    eval_config.options.include_default_metrics must also be true.
  example_weight_key: Example weight key (single-output model) or dict of
    example weight keys (multi-output model) keyed by output name.
  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.
  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: Model tags (e.g. 'serve' for serving or 'eval' for EvalSavedModel).
  eval_config: Eval config. Only used for setting default tags.
def default_eval_shared_model(eval_saved_model_path: Text, add_metrics_callbacks: Optional[List[types.AddMetricsCallbackType]]=None, include_default_metrics: Optional[bool]=True, example_weight_key: Optional[Union[(Text, Dict[(Text, Text)])]]=None, additional_fetches: Optional[List[Text]]=None, blacklist_feature_fetches: Optional[List[Text]]=None, tags: Optional[List[Text]]=None, eval_config: Optional[config.EvalConfig]=None) -> types.EvalSharedModel:
    'Returns default EvalSharedModel.\n\n  Args:\n    eval_saved_model_path: Path to EvalSavedModel.\n    add_metrics_callbacks: Optional list of callbacks for adding additional\n      metrics to the graph (see EvalSharedModel for more information on how to\n      configure additional metrics). Metrics for example count and example\n      weights will be added automatically.\n    include_default_metrics: True to include the default metrics that are part\n      of the saved model graph during evaluation. Note that\n      eval_config.options.include_default_metrics must also be true.\n    example_weight_key: Example weight key (single-output model) or dict of\n      example weight keys (multi-output model) keyed by output name.\n    additional_fetches: Prefixes of additional tensors stored in\n      signature_def.inputs that should be fetched at prediction time. The\n      "features" and "labels" tensors are handled automatically and should not\n      be included.\n    blacklist_feature_fetches: List of tensor names in the features dictionary\n      which should be excluded from the fetches request. This is useful in\n      scenarios where features are large (e.g. images) and can lead to excessive\n      memory use if stored.\n    tags: Model tags (e.g. \'serve\' for serving or \'eval\' for EvalSavedModel).\n    eval_config: Eval config. Only used for setting default tags.\n  '
    if (tags is None):
        if eval_config:
            signatures = [s.signature_name for s in eval_config.model_specs]
            if (eval_constants.EVAL_TAG in signatures):
                if (not all(((s == eval_constants.EVAL_TAG) for s in signatures))):
                    tf.compat.v1.logging.warning('mixture of eval and non-eval signatures used: eval_config={}'.format(eval_config))
                tags = [eval_constants.EVAL_TAG]
            else:
                tags = [tf.saved_model.SERVING]
        else:
            tags = [eval_constants.EVAL_TAG]
    if (tags == [eval_constants.EVAL_TAG]):
        if (not add_metrics_callbacks):
            add_metrics_callbacks = []
        example_count_callback = post_export_metrics.example_count()
        add_metrics_callbacks.append(example_count_callback)
        if example_weight_key:
            if isinstance(example_weight_key, dict):
                for (output_name, key) in example_weight_key.items():
                    example_weight_callback = post_export_metrics.example_weight(key, metric_tag=output_name)
                    add_metrics_callbacks.append(example_weight_callback)
            else:
                example_weight_callback = post_export_metrics.example_weight(example_weight_key)
                add_metrics_callbacks.append(example_weight_callback)
    return types.EvalSharedModel(model_path=eval_saved_model_path, add_metrics_callbacks=add_metrics_callbacks, include_default_metrics=include_default_metrics, example_weight_key=example_weight_key, additional_fetches=additional_fetches, model_loader=types.ModelLoader(tags=tags, construct_fn=model_util.model_construct_fn(eval_saved_model_path=eval_saved_model_path, add_metrics_callbacks=add_metrics_callbacks, include_default_metrics=include_default_metrics, additional_fetches=additional_fetches, blacklist_feature_fetches=blacklist_feature_fetches, tags=tags)))