| from transformers import AutoTokenizer, AutoModelForSeq2SeqLM | |
| def generate_doc(code_snippet): | |
| model_name = "trained_model" | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| model = AutoModelForSeq2SeqLM.from_pretrained(model_name) | |
| inputs = tokenizer(code_snippet, return_tensors="pt") | |
| outputs = model.generate(**inputs, max_length=128) | |
| return tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| if __name__ == "__main__": | |
| print(generate_doc("def multiply(a, b): return a * b")) | |