code: def test_gradient_accumulation(self):
      with self.strategy.scope():
          v = backend.Variable([[1.0, 2.0], [3.0, 4.0]])
          grads = backend.convert_to_tensor([[1.0, 1.0], [2.0, 2.0]])
          optimizer = SGD(
              learning_rate=1.0, gradient_accumulation_steps=3
          )
          self.assertEqual(optimizer.gradient_accumulation_steps, 3)
          self.strategy.run(
              lambda: optimizer.apply_gradients([(grads, v)])
          )
          self.assertAllClose(v, [[1.0, 2.0], [3.0, 4.0]])
          self.assertAllClose(
              optimizer._accumulated_gradients[0],
              [[1.0, 1.0], [2.0, 2.0]],
          )
          self.assertAllClose(optimizer.iterations, 1)
          self.strategy.run(
              lambda: optimizer.apply_gradients([(grads, v)])
          )
          self.assertAllClose(v, [[1.0, 2.0], [3.0, 4.0]])
          self.assertAllClose(
              optimizer._accumulated_gradients[0],
              [[2.0, 2.0], [4.0, 4.0]],
          )
          self.assertAllClose(optimizer.iterations, 2)
          self.strategy.run(
              lambda: optimizer.apply_gradients([(grads, v)])
          )
          self.assertAllClose(v, [[0.0, 1.0], [1.0, 2.0]])
          self.assertAllClose(
              optimizer._accumulated_gradients[0],
              [[0.0, 0.0], [0.0, 0.0]],
          )
          self.assertAllClose(optimizer.iterations, 3)
  
file_path: keras/keras/backend/tensorflow/optimizer_distribute_test.py
imports: import numpy
  import pytest
  import tensorflow
  from tensorflow.python.eager import context
  from keras import backend
  from keras import testing
  from keras.optimizers.sgd import SGD
label_desc: Use image node_id 10 for calling the OptimizerDistributeTest obj's underlying member method code with example usage: obj.test_gradient_accumulation() without return types