class_docstr: Hooks-based implementation of AbstractGradSampleModule
  
  Computes per-sample gradients using custom-written methods for each layer.
  See README.md for more details
code: def forbid_grad_accumulation(self):
      self.grad_accumulation_allowed = False
  
imports: from __future__ import annotations
  import logging
  import warnings
  from functools import partial
  from typing import Iterable, List, Tuple
  import torch
  import torch.nn
  from opacus.grad_sample.functorch import ft_compute_per_sample_gradient, prepare_layer
  from opacus.grad_sample.gsm_base import AbstractGradSampleModule
  from opacus.layers.dp_rnn import DPGRU, DPLSTM, DPRNN, RNNLinear
  from opacus.utils.module_utils import has_trainable_params, requires_grad, trainable_modules, trainable_parameters
label_desc: Use image node_id 14 for calling the GradSampleModule obj's underlying member method code with example usage: obj.forbid_grad_accumulation() without return types