// CHECK-NEXT: [[RESULT:%.*]] = "tf.BiasAdd"([[CONV]], [[BIAS]]) : (tensor<1x128x128x8xf32>, tensor<8xf32>) -> tensor<1x128x128x8xf32>
  // CHECK-NEXT: return [[RESULT]] : tensor<1x128x128x8xf32>
}

func.func @testDilatedDepthWiseConvWithPad(%arg0: tensor<1x128x128x3xf32>, %arg1: tensor<5x5x3x8xf32>, %arg2: tensor<8xf32>) -> tensor<1x128x128x8xf32> {
  %cst = arith.constant dense<[2, 2]> : tensor<2xi32>

  %7 = "tfl.add"(%1, %6) {tac.device = "GPU", tac.inference_type = "FLOAT", fused_activation_function = "NONE"} : (tensor<1x128x128xf32>, tensor<1x128x128xf32>) -> tensor<1x128x128xf32>
  func.return %7 : tensor<1x128x128xf32>
}

// CHECK:   func @norm1(%[[VAL_0:.*]]: tensor<1x128x128xf32>) -> tensor<1x128x128xf32> {

func.func private @func_20_GPU_FLOAT(%arg0: tensor<128x128xf32>, %arg1: tensor<3xi32>) -> tensor<1x128x128xf32> attributes {tac.device = "GPU", tac.inference_type = "FLOAT", tac.interface_name = "func_20"} {
  %0 = "tfl.reshape"(%arg0, %arg1) {tac.device = "GPU", tac.inference_type = "FLOAT"} : (tensor<128x128xf32>, tensor<3xi32>) -> tensor<1x128x128xf32>
  func.return %0 : tensor<1x128x128xf32>
}

  %3 = "tfl.mul"(%2, %six) {fused_activation_function = "NONE"} : (tensor<1x128x128x3xf32>, tensor<f32>) -> tensor<1x128x128x3xf32>
  func.return %3 : tensor<1x128x128x3xf32>
  // CHECK: %0 = "tfl.hard_swish"(%arg0) : (tensor<1x128x128x3xf32>) -> tensor<1x128x128x3xf32>
}

// CHECK-LABEL: @HardSwishPatternThree
func.func @HardSwishPatternThree(%arg0: tensor<1x128x128x3xf32>) -> tensor<1x128x128x3xf32> {

// CHECK:            %[[VAL_8:.*]] = "tf.Const"() <{value = dense<[0, 3, 1, 2]> : tensor<4xi64>}> : () -> tensor<4xi64>
// CHECK:            %[[VAL_9:.*]] = "tf.Transpose"(%[[VAL_7:.*]], %[[VAL_8:.*]]) : (tensor<1x128x128x64xf32>, tensor<4xi64>) -> tensor<1x64x128x128xf32>
// CHECK:            return %[[VAL_9:.*]] : tensor<1x64x128x128xf32>