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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_valid(self, data, label=None, weight=None, group=None, init_score=None, silent=False, params=None):
 """Create validation data align with current Dataset. Parameters data : string, numpy array, pandas DataFrame, H2O DataTable's Frame, scipy.sparse or list of numpy arrays Data source of Dataset. If string, it represents the path to txt file. label : list, numpy 1-D array, pandas Series / one-column DataFrame or None, optional (default=None) Label of the data. weight : list, numpy 1-D array, pandas Series or None, optional (default=None) Weight for each instance. group : list, numpy 1-D array, pandas Series or None, optional (default=None) Group/query size for Dataset. init_score : list, numpy 1-D array, pandas Series or None, optional (default=None) Init score for Dataset. silent : bool, optional (default=False) Whether to print messages during construction. params : dict or None, optional (default=None) Other parameters for validation Dataset. Returns ------- valid : Dataset Validation Dataset with reference to self. """

        ret = Dataset(data, label=label, reference=self,
                      weight=weight, group=group, init_score=init_score,
                      silent=silent, params=params, free_raw_data=self.free_raw_data)
        ret._predictor = self._predictor
        ret.pandas_categorical = self.pandas_categorical
        return ret