code: def print_model(model, name):
      print("=" * 10 + name + "=" * 10)
      print(model)
      for name, param in model.named_parameters():
          if torch.is_tensor(param):
              if param.dtype in [torch.float32, torch.float16]:
                  print(
                      name,
                      param.shape,
                      param.device,
                      param.dtype,
                      param.requires_grad,
                      param.mean().item(),
                      param.max().item(),
                  )
              else:
                  print(
                      name,
                      param.shape,
                      param.device,
                      param.dtype,
                      param.requires_grad,
                  )
  
file_path: peft/examples/loftq_finetuning/quantize_save_load.py
imports: import argparse
  import os
  import torch
  import torch.nn
  from transformers import AutoModelForCausalLM, AutoModelForSeq2SeqLM, AutoModelForSequenceClassification, AutoTokenizer
  from peft import LoftQConfig, LoraConfig, TaskType, get_peft_model
label_desc: Use image node_id 2 for calling a global function with example usage: print_model(model, name) without return types