code: def _clip_and_accumulate_parameter(
      p: nn.Parameter, max_grad_norm: float
  ):
      per_sample_norms = p.grad_sample.view(
          len(p.grad_sample), -1
      ).norm(2, dim=-1)
      per_sample_clip_factor = (
          max_grad_norm / (per_sample_norms + 1e-6)
      ).clamp(max=1.0)
  
      grad = contract("i,i...", per_sample_clip_factor, p.grad_sample)
      if p.summed_grad is not None:
          p.summed_grad += grad
      else:
          p.summed_grad = grad
  
imports: from __future__ import annotations
  from functools import partial
  from typing import Callable, List, Optional
  import torch
  from opt_einsum import contract
  from torch import nn
  from torch.optim import Optimizer
  from .ddpoptimizer import DistributedDPOptimizer
  from .optimizer import DPOptimizer, _generate_noise
  from .perlayeroptimizer import DPPerLayerOptimizer
label_desc: Use image node_id 1 for calling a global function with example usage: _clip_and_accumulate_parameter(p, max_grad_norm) without return types