# tfma.default_eval_shared_model

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| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/tensorflow/model-analysis/blob/v0.46.0/tensorflow_model_analysis/api/model_eval_lib.py#L364-L497) |

Returns default EvalSharedModel.

    tfma.default_eval_shared_model(
        eval_saved_model_path: str,
        add_metrics_callbacks: Optional[List[types.AddMetricsCallbackType]] = None,
        include_default_metrics: Optional[bool] = True,
        example_weight_key: Optional[Union[str, Dict[str, str]]] = None,
        additional_fetches: Optional[List[str]] = None,
        blacklist_feature_fetches: Optional[List[str]] = None,
        tags: Optional[List[str]] = None,
        model_name: str = '',
        eval_config: Optional[https://www.tensorflow.org/tfx/model_analysis/api_docs/python/tfma/EvalConfig] = None,
        custom_model_loader: Optional[https://www.tensorflow.org/tfx/model_analysis/api_docs/python/tfma/types/ModelLoader] = None,
        rubber_stamp: Optional[bool] = False,
        resource_hints: Optional[Dict[str, Any]] = None,
        backend_config: Optional[Any] = None
    ) -> https://www.tensorflow.org/tfx/model_analysis/api_docs/python/tfma/types/EvalSharedModel

### Used in the notebooks

| Used in the guide | Used in the tutorials |
|---|---|
| - [Using Counterfactual Logit Pairing with Keras](https://www.tensorflow.org/responsible_ai/model_remediation/counterfactual/guide/counterfactual_keras) | - [TensorFlow Model Analysis](https://www.tensorflow.org/tfx/tutorials/model_analysis/tfma_basic) - [Introduction to Fairness Indicators](https://www.tensorflow.org/responsible_ai/fairness_indicators/tutorials/Fairness_Indicators_Example_Colab) - [TensorFlow Constrained Optimization Example Using CelebA Dataset](https://www.tensorflow.org/responsible_ai/fairness_indicators/tutorials/Fairness_Indicators_TFCO_CelebA_Case_Study) |

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| ## 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. Only used if EvalSavedModel used. |
| `include_default_metrics` | DEPRECATED. Use eval_config.options.include_default_metrics. |
| `example_weight_key` | DEPRECATED. Use eval_config.model_specs.example_weight_key or eval_config.model_specs.example_weight_keys. |
| `additional_fetches` | Optional 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. Only used if EvalSavedModel used. |
| `blacklist_feature_fetches` | Optional 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. Only used if EvalSavedModel used. |
| `tags` | Optional model tags (e.g. 'serve' for serving or 'eval' for EvalSavedModel). |
| `model_name` | Optional name of the model being created (should match ModelSpecs.name). The name should only be provided if multiple models are being evaluated. |
| `eval_config` | Eval config. |
| `custom_model_loader` | Optional custom model loader for non-TF models. |
| `rubber_stamp` | True when this run is a first run without a baseline model while a baseline is configured, the diff thresholds will be ignored. |
| `resource_hints` | The beam resource hints to apply to the PTransform which runs inference for this model. |
| `backend_config` | Optional configuration of backend running model inference with *some* prediction extractors. |

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