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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 as_compatible_data(data, fastpath=False):
 """Prepare and wrap data to put in a Variable. - If data does not have the necessary attributes, convert it to ndarray. - If data has dtype=datetime64, ensure that it has ns precision. If it's a pandas.Timestamp, convert it to datetime64. - If data is already a pandas or xarray object (other than an Index), just use the values. Finally, wrap it up with an adapter if necessary. """

    if fastpath and getattr(data, 'ndim', 0) > 0:
        # can't use fastpath (yet) for scalars
        return _maybe_wrap_data(data)

    if isinstance(data, Variable):
        return data.data

    if isinstance(data, NON_NUMPY_SUPPORTED_ARRAY_TYPES):
        return _maybe_wrap_data(data)

    if isinstance(data, tuple):
        data = utils.to_0d_object_array(data)

    if isinstance(data, pd.Timestamp):
        # TODO: convert, handle datetime objects, too
        data = np.datetime64(data.value, 'ns')

    if isinstance(data, timedelta):
        data = np.timedelta64(getattr(data, 'value', data), 'ns')

    # we don't want nested self-described arrays
    data = getattr(data, 'values', data)

    if isinstance(data, np.ma.MaskedArray):
        mask = np.ma.getmaskarray(data)
        if mask.any():
            dtype, fill_value = dtypes.maybe_promote(data.dtype)
            data = np.asarray(data, dtype=dtype)
            data[mask] = fill_value
        else:
            data = np.asarray(data)

    # validate whether the data is valid data types
    data = np.asarray(data)

    if isinstance(data, np.ndarray):
        if data.dtype.kind == 'O':
            data = _possibly_convert_objects(data)
        elif data.dtype.kind == 'M':
            data = np.asarray(data, 'datetime64[ns]')
        elif data.dtype.kind == 'm':
            data = np.asarray(data, 'timedelta64[ns]')

    return _maybe_wrap_data(data)