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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 recommend_from_interactions( self, observed_items, k=10, exclude=None, items=None, new_user_data=None, new_item_data=None, exclude_known=True, diversity=0, random_seed=None, verbose=True):
 """ Recommend the ``k`` highest scored items based on the interactions given in `observed_items.` Parameters observed_items : SArray, SFrame, or list A list/SArray of items to use to make recommendations, or an SFrame of items and optionally ratings and/or other interaction data. The model will then recommend the most similar items to those given. If ``observed_items`` has a user column, then it must be only one user, and the additional interaction data stored in the model is also used to make recommendations. k : int, optional The number of recommendations to generate. items : SArray, SFrame, or list, optional Restricts the items from which recommendations can be made. ``items`` must be an SArray, list, or SFrame with a single column containing items, and all recommendations will be made from this pool of items. This can be used, for example, to restrict the recommendations to items within a particular category or genre. By default, recommendations are made from all items present when the model was trained. new_user_data : SFrame, optional ``new_user_data`` may give additional user data to the model. If present, scoring is done with reference to this new information. If there is any overlap with the side information present at training time, then this new side data is preferred. Must be in the same format as the user data passed to ``create``. new_item_data : SFrame, optional ``new_item_data`` may give additional item data to the model. If present, scoring is done with reference to this new information. If there is any overlap with the side information present at training time, then this new side data is preferred. Must be in the same format as the item data passed to ``create``. exclude : SFrame, optional An :class:`~turicreate.SFrame` of items or user / item pairs. The column names must be equal to the user and item columns of the main data, and it provides the model with user/item pairs to exclude from the recommendations. These user-item-pairs are always excluded from the predictions, even if exclude_known is False. exclude_known : bool, optional By default, all user-item interactions previously seen in the training data, or in any new data provided using new_observation_data.., are excluded from the recommendations. Passing in ``exclude_known = False`` overrides this behavior. diversity : non-negative float, optional If given, then the recommend function attempts chooses a set of `k` items that are both highly scored and different from other items in that set. It does this by first retrieving ``k*(1+diversity)`` recommended items, then randomly choosing a diverse set from these items. Suggested values for diversity are between 1 and 3. random_seed : int, optional If diversity is larger than 0, then some randomness is used; this controls the random seed to use for randomization. If None, then it will be different each time. verbose : bool, optional If True, print the progress of generating recommendation. Returns ------- out : SFrame A SFrame with the top ranked items for each user. The columns are: ``item_id``, *score*, and *rank*, where ``user_id`` and ``item_id`` match the user and item column names specified at training time. The rank column is between 1 and ``k`` and gives the relative score of that item. The value of score depends on the method used for recommendations. observed_items: list, SArray, or SFrame """


        column_types = self._get_data_schema()
        user_id = self.user_id
        item_id = self.item_id
        user_type = column_types[user_id]
        item_type = column_types[item_id]

        if not hasattr(self, "_implicit_user_name"):
            import hashlib
            import time
            self._implicit_user_name = None #("implicit-user-%s"
#                                        % hashlib.md5("%0.20f" % time.time()).hexdigest()[:12])

        if isinstance(observed_items, list):
            observed_items = _SArray(observed_items, dtype = item_type)
        if isinstance(observed_items, _SArray):
            observed_items = _SFrame({self.item_id : observed_items})

        if not isinstance(observed_items, _SFrame):
            raise TypeError("observed_items must be a list or SArray of items, or an SFrame of items "
                            "and optionally ratings or other interaction information.")

        # Don't modify the user's argument (if it's an SFrame).
        observed_items = observed_items.copy()

        # If a user id is present, then use that as the query user id
        # (making sure there is only one present).  If not, then use
        # the local fake user id.
        if user_id in observed_items.column_names():
            main_user_value = observed_items[user_id][0]
            if (observed_items[user_id] != main_user_value).any():
                raise ValueError("To recommend items for more than one user, use `recommend()` and "
                                 "supply new interactions using new_observation_data.")
            users = _SArray([main_user_value], dtype = user_type)

        else:
            users = _SArray([self._implicit_user_name], dtype = user_type)
            observed_items[user_id] = self._implicit_user_name

        if observed_items[user_id].dtype != user_type:
            observed_items[user_id] = observed_items[user_id].astype(user_type)

        # Check the rest of the arguments.
        if exclude is not None:
            if isinstance(exclude, list):
                exclude = _SArray(exclude, dtype = item_type)
            if isinstance(exclude, _SArray):
                exclude = _SFrame({item_id : exclude})
            if user_id not in exclude.column_names():
                exclude[user_id] = self._implicit_user_name
                exclude[user_id] = exclude[user_id].astype(user_type)

        recommendations = self.recommend(
            users                = users,
            new_observation_data = observed_items,
            k                    = k,
            items                = items,
            new_user_data        = new_user_data,
            new_item_data        = new_item_data,
            exclude_known        = exclude_known,
            diversity            = diversity,
            random_seed          = random_seed,
            verbose              = verbose)

        del recommendations[user_id]

        return recommendations