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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 drop_dims(self, drop_dims):
 """Drop dimensions and associated variables from this dataset. Parameters drop_dims : str or list Dimension or dimensions to drop. Returns ------- obj : Dataset The dataset without the given dimensions (or any variables containing those dimensions) """

        if utils.is_scalar(drop_dims):
            drop_dims = [drop_dims]

        missing_dimensions = [d for d in drop_dims if d not in self.dims]
        if missing_dimensions:
            raise ValueError('Dataset does not contain the dimensions: %s'
                             % missing_dimensions)

        drop_vars = set(k for k, v in self._variables.items()
                        for d in v.dims if d in drop_dims)

        variables = OrderedDict((k, v) for k, v in self._variables.items()
                                if k not in drop_vars)
        coord_names = set(k for k in self._coord_names if k in variables)

        return self._replace_with_new_dims(variables, coord_names)