elasticsearch_serverless/_async/client/ml.py [2818:2925]:
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        self,
        *,
        model_id: str,
        compressed_definition: t.Optional[str] = None,
        defer_definition_decompression: t.Optional[bool] = None,
        definition: t.Optional[t.Mapping[str, t.Any]] = None,
        description: t.Optional[str] = None,
        error_trace: t.Optional[bool] = None,
        filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None,
        human: t.Optional[bool] = None,
        inference_config: t.Optional[t.Mapping[str, t.Any]] = None,
        input: t.Optional[t.Mapping[str, t.Any]] = None,
        metadata: t.Optional[t.Any] = None,
        model_size_bytes: t.Optional[int] = None,
        model_type: t.Optional[
            t.Union[str, t.Literal["lang_ident", "pytorch", "tree_ensemble"]]
        ] = None,
        platform_architecture: t.Optional[str] = None,
        prefix_strings: t.Optional[t.Mapping[str, t.Any]] = None,
        pretty: t.Optional[bool] = None,
        tags: t.Optional[t.Sequence[str]] = None,
        wait_for_completion: t.Optional[bool] = None,
        body: t.Optional[t.Dict[str, t.Any]] = None,
    ) -> ObjectApiResponse[t.Any]:
        """
        .. raw:: html

          <p>Create a trained model.
          Enable you to supply a trained model that is not created by data frame analytics.</p>


        `<https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-ml-put-trained-model>`_

        :param model_id: The unique identifier of the trained model.
        :param compressed_definition: The compressed (GZipped and Base64 encoded) inference
            definition of the model. If compressed_definition is specified, then definition
            cannot be specified.
        :param defer_definition_decompression: If set to `true` and a `compressed_definition`
            is provided, the request defers definition decompression and skips relevant
            validations.
        :param definition: The inference definition for the model. If definition is specified,
            then compressed_definition cannot be specified.
        :param description: A human-readable description of the inference trained model.
        :param inference_config: The default configuration for inference. This can be
            either a regression or classification configuration. It must match the underlying
            definition.trained_model's target_type. For pre-packaged models such as ELSER
            the config is not required.
        :param input: The input field names for the model definition.
        :param metadata: An object map that contains metadata about the model.
        :param model_size_bytes: The estimated memory usage in bytes to keep the trained
            model in memory. This property is supported only if defer_definition_decompression
            is true or the model definition is not supplied.
        :param model_type: The model type.
        :param platform_architecture: The platform architecture (if applicable) of the
            trained mode. If the model only works on one platform, because it is heavily
            optimized for a particular processor architecture and OS combination, then
            this field specifies which. The format of the string must match the platform
            identifiers used by Elasticsearch, so one of, `linux-x86_64`, `linux-aarch64`,
            `darwin-x86_64`, `darwin-aarch64`, or `windows-x86_64`. For portable models
            (those that work independent of processor architecture or OS features), leave
            this field unset.
        :param prefix_strings: Optional prefix strings applied at inference
        :param tags: An array of tags to organize the model.
        :param wait_for_completion: Whether to wait for all child operations (e.g. model
            download) to complete.
        """
        if model_id in SKIP_IN_PATH:
            raise ValueError("Empty value passed for parameter 'model_id'")
        __path_parts: t.Dict[str, str] = {"model_id": _quote(model_id)}
        __path = f'/_ml/trained_models/{__path_parts["model_id"]}'
        __query: t.Dict[str, t.Any] = {}
        __body: t.Dict[str, t.Any] = body if body is not None else {}
        if defer_definition_decompression is not None:
            __query["defer_definition_decompression"] = defer_definition_decompression
        if error_trace is not None:
            __query["error_trace"] = error_trace
        if filter_path is not None:
            __query["filter_path"] = filter_path
        if human is not None:
            __query["human"] = human
        if pretty is not None:
            __query["pretty"] = pretty
        if wait_for_completion is not None:
            __query["wait_for_completion"] = wait_for_completion
        if not __body:
            if compressed_definition is not None:
                __body["compressed_definition"] = compressed_definition
            if definition is not None:
                __body["definition"] = definition
            if description is not None:
                __body["description"] = description
            if inference_config is not None:
                __body["inference_config"] = inference_config
            if input is not None:
                __body["input"] = input
            if metadata is not None:
                __body["metadata"] = metadata
            if model_size_bytes is not None:
                __body["model_size_bytes"] = model_size_bytes
            if model_type is not None:
                __body["model_type"] = model_type
            if platform_architecture is not None:
                __body["platform_architecture"] = platform_architecture
            if prefix_strings is not None:
                __body["prefix_strings"] = prefix_strings
            if tags is not None:
                __body["tags"] = tags
        __headers = {"accept": "application/json", "content-type": "application/json"}
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elasticsearch_serverless/_sync/client/ml.py [2818:2925]:
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        self,
        *,
        model_id: str,
        compressed_definition: t.Optional[str] = None,
        defer_definition_decompression: t.Optional[bool] = None,
        definition: t.Optional[t.Mapping[str, t.Any]] = None,
        description: t.Optional[str] = None,
        error_trace: t.Optional[bool] = None,
        filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None,
        human: t.Optional[bool] = None,
        inference_config: t.Optional[t.Mapping[str, t.Any]] = None,
        input: t.Optional[t.Mapping[str, t.Any]] = None,
        metadata: t.Optional[t.Any] = None,
        model_size_bytes: t.Optional[int] = None,
        model_type: t.Optional[
            t.Union[str, t.Literal["lang_ident", "pytorch", "tree_ensemble"]]
        ] = None,
        platform_architecture: t.Optional[str] = None,
        prefix_strings: t.Optional[t.Mapping[str, t.Any]] = None,
        pretty: t.Optional[bool] = None,
        tags: t.Optional[t.Sequence[str]] = None,
        wait_for_completion: t.Optional[bool] = None,
        body: t.Optional[t.Dict[str, t.Any]] = None,
    ) -> ObjectApiResponse[t.Any]:
        """
        .. raw:: html

          <p>Create a trained model.
          Enable you to supply a trained model that is not created by data frame analytics.</p>


        `<https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-ml-put-trained-model>`_

        :param model_id: The unique identifier of the trained model.
        :param compressed_definition: The compressed (GZipped and Base64 encoded) inference
            definition of the model. If compressed_definition is specified, then definition
            cannot be specified.
        :param defer_definition_decompression: If set to `true` and a `compressed_definition`
            is provided, the request defers definition decompression and skips relevant
            validations.
        :param definition: The inference definition for the model. If definition is specified,
            then compressed_definition cannot be specified.
        :param description: A human-readable description of the inference trained model.
        :param inference_config: The default configuration for inference. This can be
            either a regression or classification configuration. It must match the underlying
            definition.trained_model's target_type. For pre-packaged models such as ELSER
            the config is not required.
        :param input: The input field names for the model definition.
        :param metadata: An object map that contains metadata about the model.
        :param model_size_bytes: The estimated memory usage in bytes to keep the trained
            model in memory. This property is supported only if defer_definition_decompression
            is true or the model definition is not supplied.
        :param model_type: The model type.
        :param platform_architecture: The platform architecture (if applicable) of the
            trained mode. If the model only works on one platform, because it is heavily
            optimized for a particular processor architecture and OS combination, then
            this field specifies which. The format of the string must match the platform
            identifiers used by Elasticsearch, so one of, `linux-x86_64`, `linux-aarch64`,
            `darwin-x86_64`, `darwin-aarch64`, or `windows-x86_64`. For portable models
            (those that work independent of processor architecture or OS features), leave
            this field unset.
        :param prefix_strings: Optional prefix strings applied at inference
        :param tags: An array of tags to organize the model.
        :param wait_for_completion: Whether to wait for all child operations (e.g. model
            download) to complete.
        """
        if model_id in SKIP_IN_PATH:
            raise ValueError("Empty value passed for parameter 'model_id'")
        __path_parts: t.Dict[str, str] = {"model_id": _quote(model_id)}
        __path = f'/_ml/trained_models/{__path_parts["model_id"]}'
        __query: t.Dict[str, t.Any] = {}
        __body: t.Dict[str, t.Any] = body if body is not None else {}
        if defer_definition_decompression is not None:
            __query["defer_definition_decompression"] = defer_definition_decompression
        if error_trace is not None:
            __query["error_trace"] = error_trace
        if filter_path is not None:
            __query["filter_path"] = filter_path
        if human is not None:
            __query["human"] = human
        if pretty is not None:
            __query["pretty"] = pretty
        if wait_for_completion is not None:
            __query["wait_for_completion"] = wait_for_completion
        if not __body:
            if compressed_definition is not None:
                __body["compressed_definition"] = compressed_definition
            if definition is not None:
                __body["definition"] = definition
            if description is not None:
                __body["description"] = description
            if inference_config is not None:
                __body["inference_config"] = inference_config
            if input is not None:
                __body["input"] = input
            if metadata is not None:
                __body["metadata"] = metadata
            if model_size_bytes is not None:
                __body["model_size_bytes"] = model_size_bytes
            if model_type is not None:
                __body["model_type"] = model_type
            if platform_architecture is not None:
                __body["platform_architecture"] = platform_architecture
            if prefix_strings is not None:
                __body["prefix_strings"] = prefix_strings
            if tags is not None:
                __body["tags"] = tags
        __headers = {"accept": "application/json", "content-type": "application/json"}
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