tensorflow_similarity/models/contrastive_model.py [609:656]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
        stat_buffer_size: int = 1000,
    ) -> None:
        """Create the model index to make embeddings searchable via KNN.

        This method is normally called as part of `SimilarityModel.compile()`.
        However, this method is provided if users want to define a custom index
        outside of the `compile()` method.

        NOTE: This method sets `SimilarityModel._index` and will replace any
        existing index.

        Args:
            distance: Distance used to compute embeddings proximity. Defaults to
            'auto'.

            kv_store: How to store the indexed records.  Defaults to 'memory'.

            search: Which `Search()` framework to use to perform KNN search.
            Defaults to 'nmslib'.

            evaluator: What type of `Evaluator()` to use to evaluate index
            performance. Defaults to in-memory one.

            embedding_output: Which model output head predicts the embeddings
            that should be indexed. Defaults to None which is for single output
            model. For multi-head model, the callee, usually the
            `SimilarityModel()` class is responsible for passing the correct
            one.

            stat_buffer_size: Size of the sliding windows buffer used to compute
            index performance. Defaults to 1000.

        Raises:
            ValueError: Invalid search framework or key value store.
        """
        # check if we we need to set the embedding head
        num_outputs = len(self.output_names)
        if embedding_output is not None and embedding_output > num_outputs:
            raise ValueError(
                "Embedding_output value exceed number of model outputs"
            )

        if embedding_output is None and num_outputs > 1:
            print(
                "Embedding output set to be model output 0. ",
                "Use the embedding_output arg to override this.",
            )
            embedding_output = 0
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tensorflow_similarity/models/similarity_model.py [251:298]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
        stat_buffer_size: int = 1000,
    ) -> None:
        """Create the model index to make embeddings searchable via KNN.

        This method is normally called as part of `SimilarityModel.compile()`.
        However, this method is provided if users want to define a custom index
        outside of the `compile()` method.

        NOTE: This method sets `SimilarityModel._index` and will replace any
        existing index.

        Args:
            distance: Distance used to compute embeddings proximity. Defaults to
            'auto'.

            kv_store: How to store the indexed records.  Defaults to 'memory'.

            search: Which `Search()` framework to use to perform KNN search.
            Defaults to 'nmslib'.

            evaluator: What type of `Evaluator()` to use to evaluate index
            performance. Defaults to in-memory one.

            embedding_output: Which model output head predicts the embeddings
            that should be indexed. Default to None which is for single output
            model. For multi-head model, the callee, usually the
            `SimilarityModel()` class is responsible for passing the correct
            one.

            stat_buffer_size: Size of the sliding windows buffer used to compute
            index performance. Defaults to 1000.

        Raises:
            ValueError: Invalid search framework or key value store.
        """
        # check if we we need to set the embedding head
        num_outputs = len(self.output_names)
        if embedding_output is not None and embedding_output > num_outputs:
            raise ValueError(
                "Embedding_output value exceed number of model outputs"
            )

        if embedding_output is None and num_outputs > 1:
            print(
                "Embedding output set to be model output 0. ",
                "Use the embedding_output arg to override this.",
            )
            embedding_output = 0
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