code: def test_pure_lsh_wrapper_non_causal_masked(self, num_weights):
      with fastmath.use_backend(fastmath.Backend.JAX):
          n_heads = 5
          batch, seqlen, d_head = 3, 32, 8
          num_weights = 2
          n_hashes = 2
          d_model = n_heads * d_head
          layer = efficient_attention.PureLSHSelfAttentionWrapper(
              n_heads=n_heads,
              d_qk=d_head,
              d_v=d_head,
              causal=False,
              masked=True,
              chunk_len=8,
              n_chunks_before=1,
              n_chunks_after=0,
              n_hashes=n_hashes,
              n_buckets=4,
              bias=False,
              pure_lsh_implementation=efficient_attention.PureLSHSelfAttention,
              mode="train",
              num_weights=num_weights,
          )
  
          rng = jax.random.PRNGKey(0)
          rng, x_rng = jax.random.split(rng)
  
          input_shape = (batch, seqlen, d_model)
          x = jax.random.uniform(x_rng, input_shape, dtype=jnp.float32)
          mask = jnp.ones((batch, seqlen), dtype=jnp.int32)
  
          inp = (x, mask)
          w, s = layer.init(shapes.signature(inp))
          o = layer(inp)
  
          # Get the actual weights.
          weights = fastmath.tree_leaves(w)
          # Assert number of weights is as expected, the extra 1 is for output.
          self.assertLen(weights, num_weights + 1)
  
          # Assert each weight is of the expected shape.
          for i in range(num_weights + 1):
              self.assertEqual(weights[i].shape, (d_model, d_model))
  
          # Test that the output and the x's shape match.
          self.assertEqual(x.shape, o.shape)
  
          # Assert state is the shape expected.
          state = fastmath.tree_leaves(s)
          self.assertLen(state, 2)
          # buckets
          self.assertEqual(
              state[0].shape, (batch * n_heads, n_hashes * seqlen)
          )
          # rngs
          self.assertEqual(state[1].shape, (batch * n_heads, 2))
  
imports: from absl.testing import parameterized
  import jax
  import numpy
  from tensorflow import test
  from trax import fastmath
  from trax import shapes
  from trax.fastmath import numpy
  from trax.layers.research import efficient_attention
label_desc: Use image node_id 16 for calling the EfficientAttentionTest obj's underlying member method code with example usage: obj.test_pure_lsh_wrapper_non_causal_masked(num_weights) without return types