code: def _test_sparse_fast_inference(self, length):
      with fastmath.use_backend(fastmath.Backend.JAX):
          vocab_size = 16
          d_model = 4
          batch_size = 2
  
          encoder_decoder_attention_type = functools.partial(
              tl.MultiplicativeConvCausalAttention,
              sparsity=2,
              length_kernel_size=1,
          )
  
          model_fn = functools.partial(
              ct.ConfigurableTransformer,
              input_vocab_size=vocab_size,
              d_model=d_model,
              d_ff=8,
              n_encoder_layers=2,
              n_decoder_layers=2,
              n_heads=2,
              loss_sparsity=2,
              ff_sparsity=2,
              encoder_decoder_attention_type=encoder_decoder_attention_type,
              ff_use_sru=(1, 4),
          )
  
          inp = np.random.randint(vocab_size, size=(batch_size, length))
          out = np.zeros((batch_size, length), dtype=np.int32)
  
          test_utils.test_eval_equals_predict(
              (inp, out), model_fn, seq_tensor=1
          )
  
file_path: trax/trax/models/research/configurable_transformer_test.py
imports: import functools
  from absl.testing import absltest
  from absl.testing import parameterized
  import numpy
  from trax import fastmath
  from trax import layers
  from trax import shapes
  from trax.layers import test_utils
  from trax.models.research import configurable_transformer
label_desc: Use image node_id 7 for calling the ConfigurableTransformerTest obj's underlying member method code with example usage: obj._test_sparse_fast_inference(length) without return types