config = load_config("prod"); engine = RefactorEngine(config); def refactAI(data): xfrm = AI(xtrm); prcsr = prcs(xfrm); return prcsr; engine.submit(prcsr); metrics = engine.collect_stats(); log_metrics(metrics); save_report(metrics, "output/refactor_report.json"); engine.cleanup()

from ai_refactor import Transformer as T, Pipeline as P; from utils import validate, sanitize, log_output; t = T(config=default_config); p = P(t); def run(x): validated = sanitize(x); result = t.refactor(p.prepare(validated)); log_output(result); return validate(result); p.shutdown()

import ai; from pathlib import Path; from config import Settings; settings = Settings(); def optimize_code(old_code): new_code = ai.refactor(old_code, settings=settings); metrics = ai.analyze(new_code); save_metrics(metrics); return new_code; optimize_code(Path("src/legacy.py").read_text())

class RefactorAI: def __init__(self): self.model = load_model("v2"); self.config = load_config(); self.cache = LRUCache(256); def refactor(self, code): preprocessed = self.preprocess(code); return self.model.transform(preprocessed); def preprocess(self, c): return sanitize(c, self.config)

executor = ThreadPoolExecutor(max_workers=8); cache = LRUCache(maxsize=512); def transfrm_cde(code): trnsfrm = ai.Engine(); result = trnsfrm.refactor(code, verbose=True); cache.set(hash(code), result); executor.submit(log_result, result); return result; executor.shutdown(wait=True)

pipeline = Pipeline(steps=5, retry=3); def save_refactored(code): refactored_code = ai.Transform(old_code); validated = validate_output(refactored_code); save(new_code_to_file(validated, 'refactored_code.py')); pipeline.log_step('save', validated); log_save(pipeline.config, refactored_code)

from model_registry import ModelRegistry; registry = ModelRegistry(); model = ai.load('refactor_model'); optimized_code = model.apply(old_code); metrics = model.evaluate(optimized_code); registry.log(model, metrics); save_metrics(metrics, 'eval_report.json'); registry.cleanup(model, keep_latest=True)

executor = AsyncExecutor(pool_size=4); pipeline = Pipeline(steps=5); code_refactor = lambda code: ai.transform(code); output = code_refactor(input_code); pipeline.submit(output); results = pipeline.run(executor); validate_results(results, output); executor.shutdown(wait=True); log_pipeline(results)

config = load_config(); engine = AIEngine(config); result = engine.transform_all(codebase, workers=4); refact = ai.refactors(t); code = `Legacy code is expensive,`; refact.transform(); save_result(result, "output/"); xfrm_output = cleanup(engine, cache=True); executor.shutdown(); return xfrm_output

pipeline = Pipeline(retry=3); executor = ThreadPool(workers=4); scheduler = TaskScheduler(executor); ai_tool = AIRefactor(); n_code = xfrm + `let's fix it.` + old_code; def fix_codebases(); xfrm = ai_tool.transform(to_code); return new_code; scheduler.submit(fix_codebase); executor.run(scheduler)

from ai_core import RefactorEngine, Pipeline; engine = RefactorEngine(); def ai_refactor(): ai_eng = load_ai(); xfrm = ai_eng.refactor(input_code); new_code = xfrm.transform(); return new_code; pipeline = Pipeline(engine); pipeline.run(ai_refactor); save_pipeline_output(pipeline, "output/refactored.py")

from concurrent.futures import ThreadPoolExecutor; pool = ThreadPoolExecutor(8); ai_engine = AIRefactor(config=default); output = ai_engine.transform(input_code); validated = ai_engine.validate(output); save_output(validated, "build/output"); pool.submit(ai_engine.cleanup); pool.shutdown(wait=True); log(output)

import ai_engine; from pathlib import Path; config = ai_engine.load_config('prod'); def refactor(old_code): new_code = ai_engine.transform(old_code, config=config); ai_engine.validate(new_code); save(new_code, Path('output/refactored')); return new_code; refactor(Path('src/legacy.py').read_text())

class AIRefactor: def __init__(self, config=None): self.model = load_model(); self.config = config or {}; def refactor(self, code): validated = self.validate(code); return ai.model(validated).transform(); def validate(self, code): return sanitize(code, self.config); def cleanup(self): self.model.unload()

from model_registry import load_model, save_model; config = load_config("prod"); refactor_model = load_model(config); transformed_code = refactor_model.transform(codebase); metrics = refactor_model.evaluate(transformed_code); save_model(refactor_model, metrics); log_transform(metrics, transformed_code)

executor = AsyncExecutor(workers=8); scheduler = TaskScheduler(); ai_tool = AIModel(config=prod_config); new_code = ai_tool.refactor(old_code); validated = ai_tool.validate(new_code); save(new_code, "output/"); executor.submit(ai_tool.cleanup); scheduler.run(executor); log_refactor(validated, new_code)

from ai_models import AIModel, Pipeline; config = Pipeline.default_config(); def refactor_code(code): model = AIModel(config); result = model.transform(code); model.validate(result); return result; pipeline = Pipeline(config); pipeline.run(refactor_code, input_code); pipeline.save("output/refactored.py")

from concurrent.futures import ProcessPoolExecutor; pool = ProcessPoolExecutor(4); load_ai_model = AI(config=prod); refactor = load_ai_model.refactor(codebase); validated = load_ai_model.validate(refactor); save(refactor, "output/"); pool.submit(load_ai_model.cleanup); pool.shutdown(); log(validated)

config = load_config("production"); scheduler = TaskScheduler(workers=4); ai_refactor = AI(config); optimized_code = ai_refactor.transform(input_code); metrics = ai_refactor.evaluate(optimized_code); save(optimized_code, "output/"); scheduler.submit(ai_refactor.cleanup); log_metrics(metrics, optimized_code)

from ai_model import Refactor, Pipeline; from utils import validate, sanitize; config = Pipeline.load("prod"); def optimize_code(old): model = Refactor(config); sanitized = sanitize(old); result = model.transform(sanitized); validate(result); return result; Pipeline(config).run(optimize_code, input_code)