// CHECK:           %[[VAL_6:.*]] = "tfl.slice"(%[[VAL_5]], %[[VAL_1]], %[[VAL_2]]) : (tensor<1x4x384x32xf32>, tensor<4xi32>, tensor<4xi32>) -> tensor<1x1x384x32xf32>
// CHECK:           %[[VAL_7:.*]] = "tfl.reshape"(%[[VAL_6]], %[[VAL_4]]) : (tensor<1x1x384x32xf32>, tensor<3xi32>) -> tensor<1x384x32xf32>

  %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 @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>
  func.return %0 : tensor<1x4x384x384xf32>
}

// -----

  func.return %0 : tensor<1x64x84x32xf32>

  %0 = "tfl.custom"(%arg0, %arg1, %arg2) {custom_code = "Convolution2DTransposeBias", custom_option = #tfl<const_bytes : "0x010000000200000002000000">} : (tensor<32x4x4x128xf32>, tensor<1x32x42x128xf32>, tensor<4xi32>) -> tensor<1x64x84x32xf32>
  func.return %0 : tensor<1x64x84x32xf32>
}
// CHECK-LABEL: main

  %0 = "tfl.custom"(%arg0, %arg1, %arg2) {custom_code = "Convolution2DTransposeBias", custom_option = #tfl<const_bytes : "0x010000000200000002000000">} : (tensor<32x4x4x128xf32>, tensor<1x32x42x128xf32>, tensor<4xi32>) -> tensor<1x64x84x32xf32>
  func.return %0 : tensor<1x64x84x32xf32>
}
// CHECK-LABEL: main

  // 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

  %cst = "tfl.no_value"() {value = unit} : () -> none
  // CHECK: _arithmetic_count = 176160768 : i64
  %0 = "tfl.transpose_conv"(%arg0, %arg1, %arg2, %cst) {padding = "SAME", stride_h = 2 : i32, stride_w = 2 : i32, fused_activation_function = "NONE"} : (tensor<4xi32>, tensor<32x4x4x128xf32>, tensor<1x32x42x128xf32>, none) -> tensor<1x64x84x32xf32>
  func.return %0 : tensor<1x64x84x32xf32>
}

  %1 = "tfl.add"(%0, %cst) {fused_activation_function = "NONE"} : (tensor<1x64x84x32xf32>, tensor<32xf32>) -> tensor<1x64x84x32xf32>
  func.return %1 : tensor<1x64x84x32xf32>

  // CHECK-DAG: %[[SHAPE:.*]] = arith.constant dense<[1, 64, 84, 32]> : tensor<4xi32>

  func.func @operand_as_shape(%18: tensor<i32>, %39: tensor<1x4x4x32xf32>) -> () {
    %cst_5 = arith.constant dense<512> : tensor<i32>
    %19 = "tf.Pack"(%18, %cst_5) {N = 2 : i64, T = i32, axis = 0 : i64, device = ""} : (tensor<i32>, tensor<i32>) -> tensor<2xi32>
    // CHECK: -> tensor<1x512xf32>
    %40 = "tf.Reshape"(%39, %19) {T = f32, Tshape = i32, device = ""} : (tensor<1x4x4x32xf32>, tensor<2xi32>) -> tensor<?x?xf32>
   func.return
  }