code: def get_hvp(
      loss: torch.Tensor,
      params: Sequence[torch.Tensor],
      vectors: Sequence[torch.Tensor],
  ) -> Tuple[torch.Tensor, ...]:
      """
      Get a Hessian-Vector Product (HVP) `Hv` for each Hessian `H` of the `loss`
      with respect to the one of the parameter tensors in `params` and the corresponding
      vector `v` in `vectors`.
  
      # Parameters
  
      loss : `torch.Tensor`
          The loss calculated from the output of the model.
      params : `Sequence[torch.Tensor]`
          Tunable and used parameters in the model that we will calculate the gradient and hessian
          with respect to.
      vectors : `Sequence[torch.Tensor]`
          The list of vectors for calculating the HVP.
      """
      # Sanity check before performing element-wise multiplication
      assert len(params) == len(vectors)
      assert all(p.size() == v.size() for p, v in zip(params, vectors))
      grads = autograd.grad(
          loss, params, create_graph=True, retain_graph=True
      )
      hvp = autograd.grad(grads, params, grad_outputs=vectors)
      return hvp
  
file_path: allennlp/allennlp/interpret/influence_interpreters/simple_influence.py
imports: import logging
  from typing import List, Optional, Tuple, Union, Sequence
  import numpy
  import torch
  import torch.autograd
  from allennlp.common import Lazy
  from allennlp.common.tqdm import Tqdm
  from allennlp.data import DatasetReader, DatasetReaderInput, Instance
  from allennlp.data.data_loaders import DataLoader, SimpleDataLoader
  from allennlp.interpret.influence_interpreters.influence_interpreter import InfluenceInterpreter
  from allennlp.models.model import Model
label_desc: Use image node_id 2 for calling a global function with example usage: get_hvp(loss, params, vectors) and returns: hvp