code: def compute_instance_norm_grad_sample(
      layer: Union[
          nn.InstanceNorm1d,
          nn.InstanceNorm2d,
          nn.InstanceNorm3d,
      ],
      activations: List[torch.Tensor],
      backprops: torch.Tensor,
  ) -> Dict[nn.Parameter, torch.Tensor]:
      """
      Computes per sample gradients for InstanceNorm layers
  
      Args:
          layer: Layer
          activations: Activations
          backprops: Backpropagations
      """
      activations = activations[0]
      ret = {}
      if layer.weight.requires_grad:
          gs = F.instance_norm(activations, 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, Union
  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_instance_norm_grad_sample(layer, activations, backprops) and returns: ret