code: def _test_lsh_self_attention_deterministic_given_seed(
      self, causal=False
  ):
      # Once the initialization and the call seeds are pinned down we have
      # deterministic output.
      with fastmath.use_backend(fastmath.Backend.JAX):
          layer = efficient_attention.LSHSelfAttention(
              n_heads=5,
              d_qk=7,
              d_v=17,
              causal=causal,
              chunk_len=8,
              n_chunks_before=1,
              n_chunks_after=0,
              n_hashes=2,
              n_buckets=4,
              use_reference_code=True,
              attention_dropout=0.0,
              mode="train",
          )
          x = np.ones((3, 32, 8)).astype(np.float32)
  
          def get_output():
              _, _ = layer.init(
                  shapes.signature(x), jax.random.PRNGKey(0)
              )
              return layer(x, rng=jax.random.PRNGKey(1))
  
          ys = [get_output() for _ in range(10)]
  
          self.assertEqual(ys[0].shape, x.shape)
  
          for y in ys[1:]:
              np.testing.assert_array_almost_equal(ys[0], y, decimal=6)
  
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 11 for calling the EfficientAttentionTest obj's underlying member method code with example usage: obj._test_lsh_self_attention_deterministic_given_seed(causal) and returns: layer