code: def get_grad_required_state_dict(model: nn.Module):
      state_dict = OrderedDict()
      for name, parameter in model.named_parameters():
          if parameter.requires_grad:
              state_dict[name] = parameter.detach()
      return state_dict
  
imports: import os
  import random
  from collections import OrderedDict
  from typing import Callable, Optional
  import numpy
  import torch
  import torch.distributed
  import torch.nn
  from coati.experience_buffer import ExperienceBuffer
  from coati.models import Actor, Critic, RewardModel
  from torch.utils.data import DataLoader
  from transformers.modeling_utils import PreTrainedModel
  from transformers.tokenization_utils_base import PreTrainedTokenizerBase
  from colossalai.booster.plugin import TorchDDPPlugin
  from colossalai.booster.plugin.torch_ddp_plugin import TorchDDPModel
  from .base import Strategy
  from .sampler import DistributedSampler
label_desc: Use image node_id 1 for calling a global function with example usage: get_grad_required_state_dict(model) and returns: state_dict