code: def get_gradients(opt, loss, params):
      grads_and_vars = opt.compute_gradients(loss, params)
      grads, _ = zip(*grads_and_vars)
      return grads
  
imports: import os
  from absl.testing import parameterized
  from tensorflow.python.checkpoint import checkpoint
  from tensorflow.python.distribute import distribute_lib
  from tensorflow.python.distribute import mirrored_strategy
  from tensorflow.python.eager import context
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import tensor_conversion
  from tensorflow.python.framework import test_util
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import check_ops
  from tensorflow.python.ops import custom_gradient
  from tensorflow.python.ops import resource_variable_ops
  from tensorflow.python.ops import variables
  from tensorflow.python.platform import test
  from tensorflow.python.training import gradient_descent
  from tensorflow.python.training import momentum
  from tensorflow.python.training.experimental import loss_scale
  from tensorflow.python.training.experimental import loss_scale_optimizer
label_desc: Use image node_id 2 for calling a global function with example usage: get_gradients(opt, loss, params) and returns: grads