%3 = func.call @arg_reuse_test_2(%arg0, %0, %1) {tac.interface_name = "func2"} : (tensor<4x384x32xf32>, tensor<3xi32>, tensor<3xi32>) -> tensor<1x384x32xf32>
  func.return %2, %3 : tensor<1x384x32xf32>, tensor<1x384x32xf32>
}

// PARTIAL-LABEL: @arg_reuse_test_1

func.func @padSliceTo4D(%arg0: tensor<4x384x32xf32>) -> tensor<1x384x32xf32> {
  %0 = "tfl.pseudo_const"() {value = dense<0> : tensor<3xi32>} : () -> tensor<3xi32>
  %1 = "tfl.pseudo_const"() {value = dense<[1, 384, 32]> : tensor<3xi32>} : () -> tensor<3xi32>
  %2 = "tfl.slice"(%arg0, %0, %1) : (tensor<4x384x32xf32>, tensor<3xi32>, tensor<3xi32>) -> tensor<1x384x32xf32>
  func.return %2 : tensor<1x384x32xf32>
}

  // CHECK-NOT: tac.device tac.inference_type
  %5 = "tfl.dequantize"(%4) : (tensor<4x384x32x!quant.uniform<i8:f32, 0.19:1>>) -> tensor<4x384x32xf32>
  func.return %5 : tensor<4x384x32xf32>

}

func.func @annotateInferenceType(%arg0: tensor<1x1x384x!quant.uniform<i8:f32, 0.003:-128>>) -> tensor<1x384x384x!quant.uniform<i8:f32, 0.003:-128>>{
  // CHECK-NOT: tac.device tac.inference_type

}

// -----

func.func @testBatchMatmulHybridQuant(%arg0 : tensor<1x4x384x32xf32>, %arg1 : tensor<1x4x384x32x!quant.uniform<i8:f32, 0.11:-16>>) -> tensor<1x4x384x384xf32> {
  // CHECK: "tfl.batch_matmul"(%arg0, %arg1)
  %0 = "tfl.batch_matmul"(%arg0, %arg1) {adj_x = false, adj_y = true} : (tensor<1x4x384x32xf32>, tensor<1x4x384x32x!quant.uniform<i8:f32, 0.11:-16>>) -> tensor<1x4x384x384xf32>

// -----

// CHECK-LABEL: func @diag_part
// CHECK-SAME: %[[ARG:.*]]: tensor<4x3x4x3xf32>
func.func @diag_part(%arg0: tensor<4x3x4x3xf32>) -> tensor<4x3xf32> {
  // CHECK: %[[RS:.*]] = mhlo.reshape %[[ARG]] : (tensor<4x3x4x3xf32>) -> tensor<12x12xf32>
  // CHECK-DAG: %[[IOTA0:.*]] = "mhlo.iota"() <{iota_dimension = 0 : i64}> : () -> tensor<12x12xi32>