// CHECK: return %[[RESULT0]]
}

// CHECK-LABEL: testMatmulAsymAttributeTrue
func.func @testMatmulAsymAttributeTrue(%arg0: tensor<4x10x15xf32>, %arg1: tensor<4x15x17xf32>) -> tensor<4x10x17xf32> {
  %0 = "tfl.batch_matmul"(%arg0, %arg1) {adj_x = false, adj_y = false, asymmetric_quantize_inputs = true} : (tensor<4x10x15xf32>, tensor<4x15x17xf32>) -> tensor<4x10x17xf32>

}

func.func @matmul_batchv2(%arg0: tensor<2x10x15xf32>, %arg1: tensor<15x17xf32>) -> tensor<2x10x17xf32> {
  %0 = "tf.BatchMatMulV2"(%arg0, %arg1) {T = "tfdtype$DT_FLOAT", device = "/device:CPU:0", name = "MatMul", adj_x = false, adj_y = false} :
(tensor<2x10x15xf32>, tensor<15x17xf32>) -> tensor<2x10x17xf32>
  func.return %0 : tensor<2x10x17xf32>
// CHECK-LABEL: matmul_batchv2

  %cst = arith.constant dense<0> : tensor<2xi32>
  %cst_0 = arith.constant dense<1> : tensor<2xi32>
  %0 = "tf.StridedSlice"(%arg0, %cst, %cst, %cst_0) {begin_mask = 0 : i64, ellipsis_mask = 2 : i64, end_mask = 0 : i64, new_axis_mask = 0 : i64, shrink_axis_mask = 0 : i64} : (tensor<21x15x7xf32>, tensor<2xi32>, tensor<2xi32>, tensor<2xi32>) -> tensor<5x15x7xf32>
  func.return %0 : tensor<5x15x7xf32>

  %3 = "tf.Transpose"(%arg4, %2) : (tensor<10x4xf32>, tensor<2xi32>) -> tensor<4x10xf32>
  %4 = "tf.MatrixDiag"(%3) : (tensor<4x10xf32>) -> tensor<4x10x10xf32>
  %5 = "tf.Const"() { value = dense<0> : tensor<i64> } : () -> tensor<i64>
  %6 = "tf.ConcatV2"(%4, %5) : (tensor<4x10x10xf32>, tensor<i64>) -> tensor<40x10xf32>
  %7 = "tf.BatchMatMulV2"(%1, %6) {adj_x = false, adj_y = false} : (tensor<8x8x40xf32>, tensor<40x10xf32>) -> tensor<8x8x10xf32>