
    Design a function that calculates the total number of potential content recommendations available when analyzing n levels deep in a user's social network, where each level includes all unique connections from previous levels plus new connections at that depth. The number of potential connections at each level increases triangularly, forming a tetrahedral pattern where level n contains all accumulated connections from levels 1 to n.
    

    
def tetrahedral_number(n):
    """
    Design a function that calculates the total number of potential content recommendations available when analyzing n levels deep in a user's social network, where each level includes all unique connections from previous levels plus new connections at that depth. The number of potential connections at each level increases triangularly, forming a tetrahedral pattern where level n contains all accumulated connections from levels 1 to n.
    

    """
def tetrahedral_number(n):
    """
    Design a function that calculates the total number of potential content recommendations available when analyzing n levels deep in a user's social network, where each level includes all unique connections from previous levels plus new connections at that depth. The number of potential connections at each level increases triangularly, forming a tetrahedral pattern where level n contains all accumulated connections from levels 1 to n.
    

    """
    return n * (n + 1) * (n + 2) / 6