code: def test_decoding(self, spatial_shape, attn_dims):
      bs = 2
      num_heads = 3
      num_features = 4
      rng = random.key(0)
      key1, key2 = random.split(rng)
      inputs = random.normal(
          key1, (bs,) + spatial_shape + (num_heads * num_features,)
      )
      module = nn.MultiHeadDotProductAttention(
          num_heads=num_heads,
          qkv_features=num_heads * num_features,
          precision=lax.Precision.HIGHEST,
          deterministic=False,
          decode=False,
      )
      decode_module = module.clone(decode=True)
  
      initial_vars = decode_module.init(key2, inputs)
      state, params = pop(initial_vars, "params")
      causal_mask = nn.attention.make_causal_mask(
          jnp.ones((bs,) + spatial_shape)
      )
      y_ref = jax.jit(
          lambda x, y: module.apply(initial_vars, x, mask=y)
      )(inputs, causal_mask)
  
      # feed the inputs sequentially to simulate decoding
      def body_fn(state, x):
          y, state = decode_module.apply(
              {"params": params, **state}, x, mutable=["cache"]
          )
          return state, y
  
      # scan_in_dim supports scanning multiple dims
      _, y = jax_utils.scan_in_dim(
          body_fn, state, inputs, axis=attn_dims, keepdims=True
      )
  
      np.testing.assert_allclose(y_ref, y, atol=1e-5)
  
imports: import jax
  import jax.numpy
  import numpy
  from absl.testing import absltest, parameterized
  from jax import lax, random
  from jax.nn import initializers
  from flax import errors, jax_utils
  from flax import linen
  from flax.core import pop
label_desc: Use image node_id 8 for calling the AttentionTest obj's underlying member method code with example usage: obj.test_decoding(spatial_shape, attn_dims) and returns: state,  y