code: def test_gradient_accumulation(self):
      v = backend.Variable([[1.0, 2.0], [3.0, 4.0]])
      grads = backend.convert_to_tensor([[1.0, 1.0], [1.0, 1.0]])
      optimizer = optimizers.SGD(
          learning_rate=1.0, gradient_accumulation_steps=3
      )
      self.assertEqual(optimizer.gradient_accumulation_steps, 3)
      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], [1.0, 1.0]]
      )
      self.assertAllClose(optimizer.iterations, 1)
      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], [2.0, 2.0]]
      )
      self.assertAllClose(optimizer.iterations, 2)
      optimizer.apply_gradients([(grads, v)])
      self.assertAllClose(v, [[0.0, 1.0], [2.0, 3.0]])
      self.assertAllClose(
          optimizer._accumulated_gradients[0], [[0.0, 0.0], [0.0, 0.0]]
      )
      self.assertAllClose(optimizer.iterations, 3)
      optimizer.apply_gradients([(grads, v)])
      self.assertAllClose(v, [[0.0, 1.0], [2.0, 3.0]])
      self.assertAllClose(
          optimizer._accumulated_gradients[0], [[1.0, 1.0], [1.0, 1.0]]
      )
      self.assertAllClose(optimizer.iterations, 4)
  
file_path: keras/keras/optimizers/optimizer_test.py
imports: import numpy
  import pytest
  from keras import backend
  from keras import constraints
  from keras import layers
  from keras import models
  from keras import optimizers
  from keras import testing
label_desc: Use image node_id 8 for calling the OptimizerTest obj's underlying member method code with example usage: obj.test_gradient_accumulation() without return types