class_docstr: :class:`~opacus.optimizers.optimizer.DPOptimizer` that implements
  per layer clipping strategy and is compatible with distributed data parallel
class_docstr_tok: [":","class",":","`","~opacus.optimizers.optimizer.DPOptimizer","`","that","implements","per","layer","clipping","strategy","and","is","compatible","with","distributed","data","parallel"]
code: def _ddp_per_layer_hook(
      self, p: nn.Parameter, max_grad_norm: float, _: torch.Tensor
  ):
      _clip_and_accumulate_parameter(p, max_grad_norm)
      # Equivalent ot _check_skip_next_step but without popping because it has to be done for every parameter p
      if self._check_skip_next_step(pop_next=False):
          return
  
      if self.rank == 0:
          self._add_noise_parameter(p)
      else:
          p.grad = p.summed_grad
      self._scale_grad_parameter(p)
  
      return p.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 8 for calling the DistributedPerLayerOptimizer obj's underlying member method code with example usage: obj._ddp_per_layer_hook(p, max_grad_norm, _) and returns: p