code: def compute_group_norm_grad_sample(
      layer: nn.GroupNorm,
      activations: List[torch.Tensor],
      backprops: torch.Tensor,
  ) -> Dict[nn.Parameter, torch.Tensor]:
      """
      Computes per sample gradients for GroupNorm
  
      Args:
          layer: Layer
          activations: Activations
          backprops: Backpropagations
      """
      activations = activations[0]
      ret = {}
      if layer.weight.requires_grad:
          gs = (
              F.group_norm(activations, layer.num_groups, eps=layer.eps)
              * backprops
          )
          ret[layer.weight] = contract("ni...->ni", gs)
      if layer.bias is not None and layer.bias.requires_grad:
          ret[layer.bias] = contract("ni...->ni", backprops)
      return ret
  
imports: from typing import Dict, List
  import torch
  import torch.nn
  import torch.nn.functional
  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_group_norm_grad_sample(layer, activations, backprops) and returns: ret