def convert(
    model,
    input_names="input",
    target_name="target",
    probability="classProbability",
    input_length="auto",
):
    """
    Convert a LIBSVM model to Core ML format.

    Parameters
    ----------

    model: a libsvm model (C-SVC, nu-SVC, epsilon-SVR, or nu-SVR)
        or string path to a saved model.

    input_names: str | [str]
        Name of the input column(s).
        If a single string is used (the default) the input will be an array. The
        length of the array will be inferred from the model, this can be overridden
        using the 'input_length' parameter.

    target: str
        Name of the output column.

    probability: str
        Name of the output class probability column.
        Only used for C-SVC and nu-SVC that have been trained with probability
        estimates enabled.

    input_length: int
        Set the length of the input array.
        This parameter should only be used when the input is an array (i.e. when
        'input_name' is a string).

    Returns
    -------
    model: MLModel
        Model in Core ML format.

    Examples
    --------
    .. sourcecode:: python

        # Make a LIBSVM model
        >>> import svmutil
        >>> problem = svmutil.svm_problem([0,0,1,1], [[0,1], [1,1], [8,9], [7,7]])
        >>> libsvm_model = svmutil.svm_train(problem, svmutil.svm_parameter())

        # Convert using default input and output names
        >>> import coremltools
        >>> coreml_model = coremltools.converters.libsvm.convert(libsvm_model)

        # Save the CoreML model to a file.
        >>> coreml_model.save('./my_model.mlmodel')

        # Convert using user specified input names
        >>> coreml_model = coremltools.converters.libsvm.convert(libsvm_model, input_names=['x', 'y'])
    """
    if not (_HAS_LIBSVM):
        raise RuntimeError("libsvm not found. libsvm conversion API is disabled.")

    if isinstance(model, str):
        libsvm_model = _libsvm_util.load_model(model)
    else:
        libsvm_model = model
    if not isinstance(libsvm_model, _svmutil.svm_model):
        raise TypeError(
            "Expected 'model' of type '%s' (got %s)"
            % (_svmutil.svm_model, type(libsvm_model))
        )

    if not isinstance(target_name, str):
        raise TypeError(
            "Expected 'target_name' of type str (got %s)" % type(libsvm_model)
        )

    if input_length != "auto" and not isinstance(input_length, int):
        raise TypeError(
            "Expected 'input_length' of type int, got %s" % type(input_length)
        )

    if input_length != "auto" and not isinstance(input_names, str):
        raise ValueError(
            "'input_length' should not be used unless the input will be only one array."
        )

    if not isinstance(probability, str):
        raise TypeError(
            "Expected 'probability' of type str (got %s)" % type(probability)
        )

    return _libsvm_converter.convert(
        libsvm_model, input_names, target_name, input_length, probability
    )

    Convert a LIBSVM model to Core ML format.

    Parameters
    ----------

    model: a libsvm model (C-SVC, nu-SVC, epsilon-SVR, or nu-SVR)
        or string path to a saved model.

    input_names: str | [str]
        Name of the input column(s).
        If a single string is used (the default) the input will be an array. The
        length of the array will be inferred from the model, this can be overridden
        using the 'input_length' parameter.

    target: str
        Name of the output column.

    probability: str
        Name of the output class probability column.
        Only used for C-SVC and nu-SVC that have been trained with probability
        estimates enabled.

    input_length: int
        Set the length of the input array.
        This parameter should only be used when the input is an array (i.e. when
        'input_name' is a string).

    Returns
    -------
    model: MLModel
        Model in Core ML format.

    Examples
    --------
    .. sourcecode:: python

        # Make a LIBSVM model
        >>> import svmutil
        >>> problem = svmutil.svm_problem([0,0,1,1], [[0,1], [1,1], [8,9], [7,7]])
        >>> libsvm_model = svmutil.svm_train(problem, svmutil.svm_parameter())

        # Convert using default input and output names
        >>> import coremltools
        >>> coreml_model = coremltools.converters.libsvm.convert(libsvm_model)

        # Save the CoreML model to a file.
        >>> coreml_model.save('./my_model.mlmodel')

        # Convert using user specified input names
        >>> coreml_model = coremltools.converters.libsvm.convert(libsvm_model, input_names=['x', 'y'])
    
coremltools/converters/libsvm/__init__.py