code: def test_fast_inference_self_attention(self):
      with fastmath.use_backend(fastmath.Backend.JAX):
          common_kwargs = dict(
              n_heads=6,
              d_qk=7,
              d_v=17,
              share_qk=False,
              causal=True,
              chunk_len=5,
              n_chunks_before=1,
              n_chunks_after=0,
              attention_dropout=0.0,
              output_dropout=0.0,
          )
          test_kwargs = []
          for n_parallel_heads in [1, 3, 6, 12]:
              for use_python_loop in [True, False]:
                  test_kwargs.append(
                      dict(
                          n_parallel_heads=n_parallel_heads,
                          use_python_loop=use_python_loop,
                      )
                  )
  
          x = jax.random.uniform(
              jax.random.PRNGKey(0), (2, 10, 13), dtype=jnp.float32
          )
          input_signature = shapes.signature(x)
          self._test_fast_inference(
              efficient_attention.SelfAttention,
              x,
              input_signature,
              common_kwargs,
              *test_kwargs,
          )
  
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 10 for calling the EfficientAttentionTest obj's underlying member method code with example usage: obj.test_fast_inference_self_attention() without return types