The Traveling Salesman Problem (TSP) is a classic optimization challenge that seeks the shortest possible route for a salesman to visit each city in a list exactly once and return to the origin city. 
We will use the LEHD (Learning with Heavy Decoder) neural combinatorial optimization algorithm to solve this problem. LEHD employs a transformer-based architecture with a light encoder (1 layer) and a heavy decoder (6 layers) to sequentially construct tours. 
Your task is to design a `heuristics` function for the LEHD algorithm. This function will generate an attention bias matrix that guides the decoder's attention mechanism during node selection. 
The bias matrix should encode spatial relationships between cities to help the model make better routing decisions. 
Your goal is to minimize the objective, which is the average distance traveled by the salesman (called "score" or "objective" of solution).