code: def compute_rnn_linear_grad_sample(
      layer: RNNLinear,
      activations: List[torch.Tensor],
      backprops: torch.Tensor,
  ) -> Dict[nn.Parameter, torch.Tensor]:
      """
      Computes per sample gradients for ``RNNLinear`` layer. The RNN-like (DPLSTM, DPGRU) models
      are written using this layer as its building block.
  
      class
  
      Args:
          layer: Layer
          activations: Activations
          backprops: Backpropagations
      """
      activations = activations[0]
      ret = {}
      if layer.weight.requires_grad:
          gs = contract("n...i,n...j->nij", backprops, activations)
          ret[layer.weight] = gs
      if layer.bias is not None and layer.bias.requires_grad:
          ret[layer.bias] = contract("n...k->nk", backprops)
      return ret
  
imports: from typing import Dict, List
  import torch
  import torch.nn
  from opacus.layers.dp_rnn import RNNLinear
  from opt_einsum import contract
  from .utils import register_grad_sampler
label_desc: Use image node_id 1 for calling a global function with example usage: compute_rnn_linear_grad_sample(layer, activations, backprops) and returns: ret