}

func.func @fakeQuantArgsTrue(%arg0: tensor<8x8x8x8xf32>) -> tensor<8x8x8x8xf32> {
  %0 = "tf.FakeQuantWithMinMaxArgs"(%arg0) {min = -0.1 : f32, max = 0.2 : f32, num_bits = 5, narrow_range = true} : (tensor<8x8x8x8xf32>) -> tensor<8x8x8x8xf32>
  func.return %0 : tensor<8x8x8x8xf32>

  // CHECK-LABEL: fakeQuantArgsTrue

  %0 = stablehlo.constant() {value = dense<7.000000e+00> : tensor<8x8x3x3xf32>} : () -> tensor<8x8x3x3xf32>
  %2 = stablehlo.convolution(%arg0, %0) dim_numbers = [b, f, 0, 1]x[o, i, 0, 1]->[b, f, 0, 1], window = {pad = [[1, 1], [1, 1]]} {batch_group_count = 1 : i64, feature_group_count = 1 : i64} : (tensor<1x8x4x4xf32>, tensor<8x8x3x3xf32>) -> tensor<1x8x4x4xf32>
  return %2 : tensor<1x8x4x4xf32>
}

  func.return %0 : tensor<256x16x32x32xf32>

  // LAYOUT-LABEL: Conv2dNCHW
  // LAYOUT: "tfl.conv_2d"
}

func.func @fusedBatchNormV3(tensor<8x8x8x8xf32>, tensor<8xf32>, tensor<8xf32>, tensor<8xf32>, tensor<8xf32>) -> (tensor<8x8x8x8xf32>, tensor<8xf32>) {
^bb0(%arg0: tensor<8x8x8x8xf32>, %arg1: tensor<8xf32>, %arg2: tensor<8xf32>, %arg3: tensor<8xf32>, %arg4: tensor<8xf32>):
  // OK

// CHECK:           [[VAL_40:%.*]] = arith.constant dense<0> : tensor<1xi32>
// CHECK:           [[VAL_41:%.*]] = "tf.ReverseV2"([[VAL_0]], [[VAL_40]]) : (tensor<8x8x8xf32>, tensor<1xi32>) -> tensor<8x8x8xf32>
// CHECK:           [[VAL_6:%.*]] = arith.constant dense<[1, 0]> : tensor<2xi32>
// CHECK:           [[VAL_7:%.*]] = "tf.Transpose"([[VAL_3]], [[VAL_6]]) : (tensor<8x40xf32>, tensor<2xi32>) -> tensor<40x8xf32>

// permutated to match the new input shape.
// CHECK: (tensor<1x16x16x4xf32>, tensor<f32>) -> tensor<1x8x8x4xf32>

// Check that a `stablehlo.transpose` is added to the result to match the shape
// of the users.
// CHECK: %[[TRANSPOSE:.+]] = stablehlo.transpose %[[REDUCE_WINDOW]], dims = [0, 3, 1, 2] : (tensor<1x8x8x4xf32>) -> tensor<1x4x8x8xf32>
// CHECK: return %[[TRANSPOSE]]

// -----

func.func @testFusedBatchNormToBatchNormV3(%arg0: tensor<8x8x8x8xf32>, %arg1: tensor<8xf32>, %arg2: tensor<8xf32>, %arg3: tensor<8xf32>, %arg4: tensor<8xf32>) -> (tensor<8x8x8x8xf32>) {
  // CHECK: "tf.FusedBatchNormV3"
  // CHECK-SAME: device = "/job:localhost/replica:0/task:0/device:GPU:0"

// CHECK: %[[RESULT:.*]] = "tfl.reshape"(%arg0, %[[CONST:.*]]) : (tensor<?x1x8x3xf32>, tensor<3xi32>) -> tensor<?x8x3xf32>
// CHECK:  return %[[RESULT]]
}

func.func @ConvertSqueezeToReshapeWithDynamicDimension2(%arg0: tensor<?x1x8x3xf32>) -> tensor<1x8x3xf32> {
  %0 = "tfl.squeeze"(%arg0) {squeeze_dims = [0]}: (tensor<?x1x8x3xf32>) -> tensor<1x8x3xf32>
  func.return %0: tensor<1x8x3xf32>