message: Add profiling label to distinguish sparse FC from dense ones.
diff: @@ -15,6 +15,7 @@ limitations under the License.
  #ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_OPTIMIZED_SPARSE_OPS_FULLY_CONNECTED_H_
  #define TENSORFLOW_LITE_KERNELS_INTERNAL_OPTIMIZED_SPARSE_OPS_FULLY_CONNECTED_H_
  +#include "ruy/profiler/instrumentation.h" // from @ruy
  #include "tensorflow/lite/c/common.h"
  #include "tensorflow/lite/kernels/internal/common.h"
  #include "tensorflow/lite/kernels/internal/cppmath.h"
  @@ -31,6 +32,8 @@ inline void FullyConnectedSparseWeight(
  const RuntimeShape& weights_shape, const float* weights_data,
  const RuntimeShape& bias_shape, const float* bias_data,
  const RuntimeShape& output_shape, float* output_data) {
  + ruy::profiler::ScopeLabel label("FullyConnected");
  + ruy::profiler::ScopeLabel inner_label("Random Sparse");
  const float output_activation_min = params.float_activation_min;
  const float output_activation_max = params.float_activation_max;
  @@ -75,6 +78,8 @@ inline void FullyConnectedSparseWeight1x4(
  const RuntimeShape& weights_shape, const float* weights_data,
  const RuntimeShape& bias_shape, const float* bias_data,
  const RuntimeShape& output_shape, float* output_data) {
  + ruy::profiler::ScopeLabel label("FullyConnected");
  + ruy::profiler::ScopeLabel inner_label("1x4 Block Sparse");
  const float output_activation_min = params.float_activation_min;
  const float output_activation_max = params.float_activation_max;
  