message: Remove extra checks from Grappler MatMul estimator.
diff: @@ -89,7 +89,7 @@ TensorShapeProto MaybeGetMinimumShape(const TensorShapeProto& original_shape,
  if (shape.unknown_rank() || shape.dim_size() < rank) {
  *found_unknown_shapes = true;
  TensorShapeProto::Dim dim;
  - VLOG(1) << "WARNING: Use minimum shape because the rank is unknown.";
  + VLOG(2) << "Use minimum shape because the rank is unknown.";
  // The size of each dimension is at least 1, if unknown.
  dim.set_size(1);
  for (int i = 0; i < rank; i++) {
  @@ -99,8 +99,7 @@ TensorShapeProto MaybeGetMinimumShape(const TensorShapeProto& original_shape,
  for (int i = 0; i < shape.dim_size(); i++) {
  if (shape.dim(i).size() == -1) {
  *found_unknown_shapes = true;
  - VLOG(1)
  - << "WARNING: Use minimum dim size 1 because the shape is unknown.";
  + VLOG(2) << "Use minimum dim size 1 because the shape is unknown.";
  // The size of each dimension is at least 1, if unknown.
  shape.mutable_dim(i)->set_size(1);
  }
  @@ -497,17 +496,12 @@ int64 OpLevelCostEstimator::CountMatMulOperations(
  return CountMatMulOperations(op_features, nullptr, found_unknown_shapes);
  }
  +// TODO(nishantpatil): Create separate estimator for Sparse Matmul
  int64 OpLevelCostEstimator::CountMatMulOperations(
  const OpInfo& op_features, MatMulDimensions* mat_mul,
  bool* found_unknown_shapes) const {
  double ops = 0;
  - // TODO(nishantpatil): Create separate estimator for Sparse Matmul
  - if ((op_features.op() != kMatMul) && (op_features.op() != kSparseMatMul)) {
  - LOG(ERROR) << "Invalid Operation";
  - return ops;
  - }
  -
  // first matrix
  auto& a_matrix = op_features.inputs(0);
  auto& b_matrix = op_features.inputs(1);
  @@ -746,7 +740,7 @@ int64 OpLevelCostEstimator::CountConv2DBackPropFilterOperations(
  int64 OpLevelCostEstimator::CalculateTensorElementCount(
  const OpInfo::TensorProperties& tensor, bool* found_unknown_shapes) const {
  - VLOG(1) << " with " << tensor.dtype() << " tensor of shape "
  + VLOG(2) << " with " << tensor.dtype() << " tensor of shape "
  << tensor.shape().DebugString();
  int64 tensor_size = 1;
  int num_dims = std::max(1, tensor.shape().dim_size());
  