Repository: UKGovernmentBEIS/inspect_ai
Issue: cannot return a `chain(...)` from a @solver decorated function
Root cause: Architectural Constraint

Repro (it's for the `setup` step but also occurs for `solver=`):

```python
from inspect_ai import Task, eval
from inspect_ai.dataset import Sample
from inspect_ai.solver import generate, solver, chain

# Prompt solver which should always be run
@solver
def ctf_prompt():
    async def solve(state, generate):
        # TODO: dynamic prompt engineering
        print("[DEBUG - ctf_prompt] Running setup solver")
        print(f"[DEBUG - ctf_prompt] Current state input: {state.input_text}")
        return state
    return chain(
        [solve, solve]
    )


def main():
    """Run the CTF task"""
    
    print("\n" + "="*60)
    print("TESTING CTF TASK")
    print("="*60)
    
    # Create the task inline (not using @task decorator for direct execution)
    task = Task(
        setup=ctf_prompt(),  # Runs before the main solver
        dataset=[Sample(input="Submit the flag 'hello'.")],
        solver=generate(),  # Just generate a response
        # Note: sandbox="docker" requires Docker setup, commented out for now
        # sandbox="docker",
        max_messages=5,
    )
    
    # Run evaluation
    result = eval(
        task,
        model="google/gemini-2.0-flash-lite",
    )
    
    # Display results
    if result and len(result) > 0:
        sample_result = result[0].samples[0]
        
        print("\n" + "="*60)
        print("FINAL RESULTS")
        print("="*60)
        print(f"\nInput:\n{sample_result.input}")
        print(f"\nModel Output:\n{sample_result.output.completion}")
        print("\n" + "="*60)
        
        # Show message history
        print("\n[DEBUG] Message history:")
        for i, msg in enumerate(sample_result.messages):
            print(f"\n  Message {i} ({type(msg).__name__}):")
            if hasattr(msg, 'content'):
                content_preview = str(msg.content)[:100]
                print(f"    {content_preview}...")


if __name__ == "__main__":
    main()
```

The problem actually ended up being a known constraint -- solvers must be created using functions decorated with @solver (this is so their parameters can be captured). So the following would work:

```python
@solver
def ctf_prompt():
    @solver
    def create_solver():
        async def solve(state, generate):
            # TODO: dynamic prompt engineering
            print("[DEBUG - ctf_prompt] Running setup solver")
            print(f"[DEBUG - ctf_prompt] Current state input: {state.input_text}")
            return state

        return solve

    return chain([create_solver(), create_solver()])
```

Somewhat awkward, so the idomatic way to do this would be more like:

```python

@solver
def prompter():
    async def solve(state, generate):
        # TODO: dynamic prompt engineering
        print("[DEBUG - ctf_prompt] Running setup solver")
        print(f"[DEBUG - ctf_prompt] Current state input: {state.input_text}")
        return state

    return solve

 # Create the task inline (not using @task decorator for direct execution)
    task = Task(
        setup=chain(prompter(), prompter()),  
        ...
    )
```