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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def convert(sk_obj, input_features = None, output_feature_names = None):
 """ Convert scikit-learn pipeline, classifier, or regressor to Core ML format. Parameters sk_obj: model | [model] of scikit-learn format. Scikit learn model(s) to convert to a Core ML format. The input model may be a single scikit learn model, a scikit learn pipeline model, or a list of scikit learn models. Currently supported scikit learn models are: - Linear and Logistic Regression - LinearSVC and LinearSVR - SVC and SVR - NuSVC and NuSVR - Gradient Boosting Classifier and Regressor - Decision Tree Classifier and Regressor - Random Forest Classifier and Regressor - Normalizer - Imputer - Standard Scaler - DictVectorizer - One Hot Encoder The input model, or the last model in a pipeline or list of models, determines whether this is exposed as a Transformer, Regressor, or Classifier. Note that there may not be a one-to-one correspondence between scikit learn models and which Core ML models are used to represent them. For example, many scikit learn models are embedded in a pipeline to handle processing of input features. input_features: str | dict | list Optional name(s) that can be given to the inputs of the scikit-learn model. Defaults to 'input'. Input features can be specified in a number of forms. - Single string: In this case, the input is assumed to be a single array, with the number of dimensions set using num_dimensions. - List of strings: In this case, the overall input dimensions to the scikit-learn model is assumed to be the length of the list. If neighboring names are identical, they are assumed to be an input array of that length. For example: ["a", "b", "c"] resolves to [("a", Double), ("b", Double), ("c", Double)]. And: ["a", "a", "b"] resolves to [("a", Array(2)), ("b", Double)]. - Dictionary: Where the keys are the names and the indices or ranges of feature indices. In this case, it's presented as a mapping from keys to indices or ranges of contiguous indices. For example, {"a" : 0, "b" : [2,3], "c" : 1} Resolves to [("a", Double), ("c", Double), ("b", Array(2))]. Note that the ordering is determined by the indices. - List of tuples of the form `(name, datatype)`. Here, `name` is the name of the exposed feature, and `datatype` is an instance of `String`, `Double`, `Int64`, `Array`, or `Dictionary`. output_feature_names: string or list of strings Optional name(s) that can be given to the inputs of the scikit-learn model. The output_feature_names is interpreted according to the model type: - If the scikit-learn model is a transformer, it is the name of the array feature output by the final sequence of the transformer (defaults to "output"). - If it is a classifier, it should be a 2-tuple of names giving the top class prediction and the array of scores for each class (defaults to "classLabel" and "classScores"). - If it is a regressor, it should give the name of the prediction value (defaults to "prediction"). Returns ------- model:MLModel Returns an MLModel instance representing a Core ML model. Examples -------- .. sourcecode:: python # Load data # Train a model # Convert and save the scikit-learn model ["bedroom", "bath", "size"], "price") """


    # This function is just a thin wrapper around the internal converter so
    # that sklearn isn't actually imported unless this function is called
    from ...models import MLModel

    # NOTE: Providing user-defined class labels will be enabled when
    # several issues with the ordering of the classes are worked out.  For now,
    # to use custom class labels, directly import the internal function below.
    from ._converter_internal import _convert_sklearn_model
    spec = _convert_sklearn_model(
            sk_obj, input_features, output_feature_names, class_labels = None)

    return MLModel(spec)