code: def prepare_layer(layer, batch_first=True):
      """
      Prepare a layer to compute grad samples using functorch.
      The grad samples are computed by redoing the forward and
      backward passes on the functional version of the module.
  
      Args:
          layer: the layer to prepare
          batch_first: whether the input is batch_first or not
      """
      if len(list(layer.buffers())) > 0:
          raise NotImplementedError(
              "This layer has buffers and is not supported by Opacus"
          )
      if type(layer) is nn.EmbeddingBag:
          raise NotImplementedError(
              "Functorch does not support EmbeddingBag yet"
          )
  
      flayer, _ = make_functional(layer)
  
      def compute_loss_stateless_model(params, activations, backprops):
          if batch_first or type(layer) is RNNLinear:
              batched_activations = activations.unsqueeze(0)
              batched_backprops = backprops.unsqueeze(0)
          else:
              # If batch_first is False, the batch dimension is the second dimension
              batched_activations = activations.unsqueeze(1)
              batched_backprops = backprops.unsqueeze(1)
  
          output = flayer(params, batched_activations)
          loss = (output * batched_backprops).sum()
          return loss
  
      ft_compute_grad = grad(compute_loss_stateless_model)
      # Note that the vmap is done on the first dimension, regardless of batch_first
      # This is because the activations and backprops given by the GradSampleModule
      # are always batch_first=True
      layer.ft_compute_sample_grad = vmap(
          ft_compute_grad, in_dims=(None, 0, 0)
      )
  
file_path: opacus/opacus/grad_sample/functorch.py
imports: import copy
  import torch
  import torch.nn
  from opacus.layers.dp_rnn import RNNLinear
  from torch.func import grad, vmap
label_desc: Use image node_id 2 for calling a global function with example usage: prepare_layer(layer, batch_first) and returns: loss