code: def jvp(f, x, v):
      "Simulate forward-mode AD using two reverse-mode AD"
      x = tuple(xx.requires_grad_(True) for xx in x)
      v = tuple(vv.requires_grad_(True) for vv in v)
      y = f(*x)
      grad_x = torch.autograd.grad(y, x, v, create_graph=True)
      jvp_val = torch.autograd.grad(grad_x, v, v)
      return y, jvp_val
  
imports: import unittest
  import torch
  from linear_operator.operators import CholLinearOperator, TriangularLinearOperator
  import gpytorch
  from gpytorch.constraints import GreaterThan
  from gpytorch.distributions import MultivariateNormal
  from gpytorch.variational import NaturalVariationalDistribution, TrilNaturalVariationalDistribution
label_desc: Use image node_id 1 for calling a global function with example usage: jvp(f, x, v) and returns: y,  jvp_val