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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 create(dataset, transformers):
 """ Create a Transformer object to transform data for feature engineering. Parameters dataset : SFrame The dataset to use for training the model. transformers: Transformer | list[Transformer] An Transformer or a list of Transformers. See Also -------- turicreate.toolkits.feature_engineering._feature_engineering._TransformerBase Examples -------- .. sourcecode:: python # Create data. QuadraticFeatures, OneHotEncoder # Create a single transformer. OneHotEncoder(max_categories = 10)) # Create a chain of transformers. QuadraticFeatures(), FeatureHasher() ]) # Create a chain of transformers with names for each of the steps. ('quadratic', QuadraticFeatures()), ('hasher', FeatureHasher()) ]) """

    err_msg = "The parameters 'transformers' must be a valid Transformer object."
    cls = transformers.__class__

    _raise_error_if_not_sframe(dataset, "dataset")

    # List of transformers.
    if (cls == list):
        transformers = TransformerChain(transformers)
    # Transformer.
    else:
        if not issubclass(cls, TransformerBase):
            raise TypeError(err_msg)
    # Fit and return
    transformers.fit(dataset)
    return transformers