code: def _forward_over_back_hvp(model, images, labels, vector):
      with forwardprop.ForwardAccumulator(
          model.trainable_variables, vector
      ) as acc:
          with tf.GradientTape() as grad_tape:
              logits = model(images, training=True)
              loss = tf.compat.v1.losses.softmax_cross_entropy(
                  logits=logits, onehot_labels=labels
              )
          grads = grad_tape.gradient(loss, model.trainable_variables)
      return acc.jvp(grads)
  
imports: import gc
  import time
  from absl.testing import parameterized
  import tensorflow
  from tensorflow.python.eager import forwardprop
  from tensorflow.python.eager.benchmarks.resnet50 import resnet50
  from tensorflow.python.eager.benchmarks.resnet50 import resnet50_test_util
label_desc: Use image node_id 1 for calling a global function with example usage: _forward_over_back_hvp(model, images, labels, vector) and returns: acc