message: Add fused/non-fused inference/training overhead benchmarks for
  BatchNormalization.
diff: @@ -246,10 +246,40 @@ class MicroBenchmarksBase(test.Benchmark):
  self._run(fn, 10000)
  - def benchmark_layers_normalization_batch_normalization_overhead(self):
  + def benchmark_layers_batch_norm_fused_inf(self):
  - layer = normalization.BatchNormalization()
  - x = array_ops.ones((1, 1))
  + layer = normalization.BatchNormalization(fused=True)
  + x = array_ops.ones((1, 1, 1, 1))
  +
  + def fn():
  + layer(x)
  +
  + self._run(fn, 10000)
  +
  + def benchmark_layers_batch_norm_fused_train(self):
  +
  + layer = normalization.BatchNormalization(fused=True)
  + x = array_ops.ones((1, 1, 1, 1))
  +
  + def fn():
  + layer(x, training=True)
  +
  + self._run(fn, 10000)
  +
  + def benchmark_layers_batch_norm_nonfused_inf(self):
  +
  + layer = normalization.BatchNormalization(fused=False)
  + x = array_ops.ones((1, 1, 1, 1))
  +
  + def fn():
  + layer(x)
  +
  + self._run(fn, 10000)
  +
  + def benchmark_layers_batch_norm_nonfused_train(self):
  +
  + layer = normalization.BatchNormalization(fused=False)
  + x = array_ops.ones((1, 1, 1, 1))
  def fn():
  layer(x, training=True)
  