docstr: Test chunking gradient here to exercise accelerator memory usage.
docstr_tok: ["Test","chunking","gradient","here","to","exercise","accelerator","memory","usage","."]
code: def test_chunk_grad_memory(self):
      """Test chunking gradient here to exercise accelerator memory usage."""
      layer = tl.Serial(tl.Dense(1024 * 1024), tl.Dense(24))
      chunked = tl.Chunk(layer, 256)
  
      @fastmath.jit
      def mock_training_step(x, weights, state, rng):
          def compute_mock_loss(weights):
              logits, new_state = chunked.pure_fn(
                  x, weights, state, rng
              )
              loss = fastmath.numpy.mean(logits)
              return loss, (new_state, logits)
  
          gradients, (new_state, logits) = fastmath.grad(
              compute_mock_loss, has_aux=True
          )(weights)
          new_weights = fastmath.nested_map_multiarg(
              lambda w, g: w - 1e-4 * g, weights, gradients
          )
          return new_weights, new_state, logits
  
      x = np.random.uniform(size=(32 * 1024, 16))
      chunked.init(shapes.signature(x))
      weights, _, logits = mock_training_step(
          x, chunked.weights, chunked.state, fastmath.random.get_prng(0)
      )
      self.assertEqual(logits.shape, (32 * 1024, 24))
      self.assertEqual(weights[1][0][0][0].shape, (16, 1024 * 1024))
  
file_path: trax/trax/layers/acceleration_test.py
imports: from absl.testing import absltest
  from jax import config
  import numpy
  from trax import fastmath
  from trax import layers
  from trax import shapes
label_desc: Use image node_id 5 for calling the AccelerationTest obj's underlying member method code with example usage: obj.test_chunk_grad_memory() and returns: new_weights,  new_state,  logits, loss,  new_state,  logits