%1 = "tf.ReadVariableOp"(%0) : (tensor<!tf_type.resource<tensor<4x4x4xf32>>>) -> tensor<4x4x4xf32>
  "tf_device.cluster_func"(%1) {func = @cluster_func, use_spmd_for_xla_partitioning = true, num_cores_per_replica = 8 : i64} : (tensor<4x4x4xf32>) -> ()
  func.return
}

func.func @cluster_func(%arg0: tensor<4x4x4xf32>) {
  func.return
}

// -----

func.func @broadcast_to_to_reshape(%arg0: tensor<4x4x4xf32>, %arg1 : tensor<4xi32>) -> tensor<1x4x4x4xf32> {
  %0 = "tfl.broadcast_to"(%arg0, %arg1) : (tensor<4x4x4xf32>, tensor<4xi32>) -> tensor<1x4x4x4xf32>
  // CHECK: "tfl.reshape"
  // CHECK-SAME: (tensor<4x4x4xf32>, tensor<4xi32>) -> tensor<1x4x4x4xf32>
  func.return %0 : tensor<1x4x4x4xf32>
}

  return %2 : tensor<1x8x4x4xf32>
}
// CHECK-DAG: %[[CONST:.+]] = stablehlo.constant {{.*}} : tensor<3x3x8x8xf32>
// CHECK-DAG: %[[TRANSPOSE_0:.+]] = stablehlo.transpose %[[ARG]], dims = [0, 2, 3, 1] : (tensor<1x8x4x4xf32>) -> tensor<1x4x4x8xf32>

// CHECK:           %[[VAL_6:.*]] = arith.constant dense<[0, 3, 1, 2]> : tensor<4xi32>
// CHECK:           %[[VAL_7:.*]] = "tfl.transpose"(%[[VAL_5]], %[[VAL_6]]) : (tensor<1x4x4x3xf32>, tensor<4xi32>) -> tensor<1x3x4x4xf32>
// CHECK:           %[[VAL_8:.*]] = "tf.Identity"(%[[VAL_7]]) {device = ""} : (tensor<1x3x4x4xf32>) -> tensor<*xf32>

func.func @broadcast_multi_dim_add(%arg0: tensor<4x1x1xi32>, %arg1: tensor<4x4x4x4xi32>) -> tensor<4x4x4x4xi32> {
  // CHECK-NEXT: %[[LHS_BCAST:.+]] = "mhlo.broadcast_in_dim"(%arg0) <{broadcast_dimensions = dense<[1, 2, 3]> : tensor<3xi64>}>
  // CHECK-NEXT: mhlo.add %[[LHS_BCAST]], %arg1
  %0 = "tf.AddV2"(%arg0, %arg1) : (tensor<4x1x1xi32>, tensor<4x4x4x4xi32>) -> tensor<4x4x4x4xi32>
  func.return %0: tensor<4x4x4x4xi32>
}

// CHECK-LABEL: func @add_dynamic

        %0 = stablehlo.constant dense<2.000000e+00> : tensor<3x3x4x4xf32>
        %1 = stablehlo.convolution(%arg0, %0) dim_numbers = [b, 0, 1, f]x[0, 1, i, o]->[b, 0, 1, f], window = {pad = [[1, 1], [1, 1]]} {batch_group_count = 1 : i64, feature_group_count = 1 : i64} : (tensor<1x3x3x4xf32>, tensor<3x3x4x4xf32>) -> tensor<1x3x3x4xf32>

func.func @main(tensor<4x4x4xf32>, tensor<4x4xf32>, tensor<4x4xf32>, tensor<4x4xf32>, tensor<4x4xf32>, tensor<4x4xf32>, tensor<4x4xf32>, tensor<4x4xf32>, tensor<4x4xf32>, tensor<4xf32>, tensor<4xf32>, tensor<4xf32>, tensor<4xf32>, tensor<4xf32>, tensor<4xf32>, tensor<4xf32>, tensor<4x4xf32>, tensor<4xf32>, tensor<4x4xf32>, tensor<4x4xf32>, tensor<4x4xf32>, tensor<4x4xf32>) -> tensor<4x4x4xf32> {
// CHECK: {
// CHECK-NEXT:   version: 3,

    %9 = stablehlo.convert %3 : (tensor<3x3x4x4xi8>) -> tensor<3x3x4x4xf32>
    %10 = stablehlo.convolution(%8, %9) dim_numbers = [b, 0, 1, f]x[0, 1, i, o]->[b, 0, 1, f], window = {pad = [[1, 1], [1, 1]]} {batch_group_count = 1 : i64, feature_group_count = 1 : i64} : (tensor<1x3x3x4xf32>, tensor<3x3x4x4xf32>) -> tensor<1x3x3x4xf32>
    %11 = stablehlo.reshape %2 : (tensor<1x1x1x1xi8>) -> tensor<1xi8>

func.func @CheckNumericVerifyWholeModel(%arg0: tensor<1x4x4x3xf32>) -> tensor<1x1x1x3xf32> {
  %0 = "tfl.quantize"(%arg0) {qtype = tensor<1x4x4x3x!quant.uniform<i8:f32, 0.1>>, volatile} : (tensor<1x4x4x3xf32>) -> tensor<1x4x4x3x!quant.uniform<i8:f32, 0.1>>
  %1 = "tfl.dequantize"(%0) : (tensor<1x4x4x3x!quant.uniform<i8:f32, 0.1>>) -> tensor<1x4x4x3xf32>

    // CHECK: return %[[RESULT]] : tensor<4x4x4xf32>

    %0 = "tf.Atan2"(%arg0, %arg1) : (tensor<4x1xf32>, tensor<4x1x4xf32>) -> tensor<4x4x4xf32>
    func.return %0: tensor<4x4x4xf32>
  }

  // CHECK-LABEL: func @ternary_op
  func.func @ternary_op(%arg0: tensor<2xi1>, %arg1: tensor<2xi32>, %arg2: tensor<2xi32>) -> tensor<2xi32> {
    // CHECK: mhlo.select %arg0, %arg1, %arg2