code: def print_trainable_parameters(model):
      trainable_params = 0
      all_param = 0
      for _, param in model.named_parameters():
          all_param += param.numel()
          if param.requires_grad:
              trainable_params += param.numel()
      print(
          f"trainable params: {trainable_params} || all params: {all_param} || trainable%: {100 * trainable_params / all_param}"
      )
  
file_path: trl/examples/research_projects/tools/triviaqa.py
imports: import os
  from dataclasses import dataclass, field
  from typing import Optional
  import torch
  from datasets import load_dataset
  from peft import LoraConfig
  from transformers import AutoTokenizer, HfArgumentParser, load_tool
  from trl import AutoModelForCausalLMWithValueHead, PPOConfig, PPOTrainer, TextEnvironment
label_desc: Use image node_id 4 for calling a global function with example usage: print_trainable_parameters(model) without return types