code: def hessian_vector_product(f, params):
      # for H = grad**2 f, compute Hx
      g = flat_grad(f, params)
      x = tf.placeholder(tf.float32, shape=g.shape)
      return x, flat_grad(tf.reduce_sum(g * x), params)
  
imports: import numpy
  import tensorflow
  import scipy.signal
  from gym.spaces import Box, Discrete
label_desc: Use image node_id 16 for calling a global function with example usage: hessian_vector_product(f, params) and returns: x,  flat_grad