Return code: 0
Duration: 141.2s

=== STDOUT ===
🚀 Running coder
📦 Dynamic packages: clingo
🤖 Creating agent with model: anthropic/claude-sonnet-4.5
🤖 Using model: anthropic/claude-sonnet-4.5
  python_exec: print("=== Task 1: Problem Analysis ===")
  python_exec: analyzing/aggregating data
  python_exec: import clingo
  python_exec: defining function on_model()
  python_exec: analyzing/aggregating data
  python_exec: defining function on_model()
  python_exec: print("=== Task 6: Format Output ===")
  python_exec: print("=== Task 7: Verify Solution ===")
  save_code: {'code': 'import clingo\nimpor...
  python_exec: executing file 033_independent_set_hard_task_code.py
  report_issue: {'text': 'All is fine - no iss...

Perfect! I have successfully completed the task. Here's a summary:              


                                Solution Summary                                

I created an ASP solution using the clingo Python API that finds the maximum    
independent set in a graph with complex constraints.                            

                          Key Implementation Details:                           

 1 ASP Model Structure:                                                         
    • Defined vertices (1-24), zones (1-3), and types (core/support/peripheral) 
      as facts                                                                  
    • Added bidirectional edges                                                 
    • Used choice rules to select vertices for the independent set              
 2 Constraints Implemented:                                                     
    • Independence: No edges between selected vertices                          
    • Core limit: At most 2 core vertices                                       
    • Core-support dependency: Each core vertex requires ≥1 support from same   
      zone                                                                      
    • Zone exclusion: Peripheral from zone 1 excludes all zone 3 vertices       
    • Peripheral headcount: Peripheral count ≤ core count (using auxiliary      
      predicates)                                                               
 3 Optimization:                                                                
    • Maximized the size of the independent set using #maximize                 
 4 Result:                                                                      
    • Found optimal solution of size 7: vertices [1, 3, 9, 11, 14, 16, 19]      
    • Contains 2 core, 3 support, and 2 peripheral vertices                     
    • All constraints verified and satisfied                                    
    • Output formatted as required JSON                                         

The solution executes quickly and produces the expected maximum independent set 
size of 7.                                                                      

==================================================
📊 Execution Statistics
==================================================

🔧 Tool Usage:
  python_exec            9 calls
  report_issue           1 calls
  save_code              1 calls

💬 Token Consumption:
  Input tokens:        174,970
  Output tokens:       7,643
  Total tokens:        182,613

⏱️  Execution time: 2m 20.3s
==================================================


=== STDERR ===
<block>:66:52-58: error: syntax error, unexpected #count

