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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 _broadcast_item(self, row_lookup, col_lookup, item, to_shape):
 """Use numpy to broadcast or reshape item. Notes: - Numpy is memory efficient, there shouldn't be performance issue. """

        # It is valid to pass a DataFrame or Series to __setitem__ that is larger than
        # the target the user is trying to overwrite. This
        if isinstance(item, (pandas.Series, pandas.DataFrame, DataFrame)):
            if not all(idx in item.index for idx in row_lookup):
                raise ValueError(
                    "Must have equal len keys and value when setting with "
                    "an iterable"
                )
            if hasattr(item, "columns"):
                if not all(idx in item.columns for idx in col_lookup):
                    raise ValueError(
                        "Must have equal len keys and value when setting "
                        "with an iterable"
                    )
                item = item.reindex(index=row_lookup, columns=col_lookup)
            else:
                item = item.reindex(index=row_lookup)
        try:
            item = np.array(item)
            if np.prod(to_shape) == np.prod(item.shape):
                return item.reshape(to_shape)
            else:
                return np.broadcast_to(item, to_shape)
        except ValueError:
            from_shape = np.array(item).shape
            raise ValueError(
                "could not broadcast input array from shape {from_shape} into shape "
                "{to_shape}".format(from_shape=from_shape, to_shape=to_shape)
            )