import multiprocessing
from functools import reduce
import numpy as np

def map_function({{ loop_var }}):
    {{ body }}
    return result

def reduce_function(results):
    return reduce(lambda x, y: x + y, results)

if __name__ == '__main__':
    # Determine number of processors
    num_procs = multiprocessing.cpu_count()
    
    # Get data from the iteration expression
    data = {{ iter_expr }}
    
    # Create chunks for spatial domain partitioning
    chunks = np.array_split(data, num_procs) if hasattr(data, '__len__') else [data]
    
    # Create process pool
    with multiprocessing.Pool() as pool:
        # Map phase: Process chunks in parallel
        mapped_chunks = pool.map(map_function, chunks)
        
        # Reduce phase: Combine results from all chunks
        final_result = reduce_function(mapped_chunks)
    
    print(f"Final result: {final_result}")

