}

// -----

// CHECK-LABEL: testReverseV2
func.func @testReverseV2(%arg0: tensor<1x2x3x4xf32>, %arg1 : tensor<2xi32>) -> tensor<1x2x3x4xf32> {
  // CHECK: "tfl.reverse_v2"(%arg0, %arg1)
  %0 = "tfl.reverse_v2"(%arg0, %arg1): (tensor<1x2x3x4xf32>, tensor<2xi32>) -> tensor<1x2x3x4xf32>
  func.return %0 : tensor<1x2x3x4xf32>
}

// -----

// test select
// CHECK-LABEL: testSelect

// CHECK: %[[CONV:.+]] = stablehlo.convolution(%[[ARG0]], %[[DQ]])
// CHECK{LITERAL}: dim_numbers = [b, 0, 1, f]x[i, 0, 1, o]->[b, 0, 1, f], window = {pad = [[1, 1], [1, 1]]} {batch_group_count = 1 : i64, feature_group_count = 4 : i64}
// CHECK-SAME: (tensor<1x3x3x4xf32>, tensor<1x3x3x4xf32>) -> tensor<1x3x3x4xf32>

// CHECK:  return
}

func.func @reverse_v2(%arg0: tensor<1x2x3x4xf32>, %arg1: tensor<1xi32>) -> tensor<1x2x3x4xf32> {
  %0 = "tf.ReverseV2"(%arg0, %arg1) : (tensor<1x2x3x4xf32>, tensor<1xi32>) -> tensor<1x2x3x4xf32>
  func.return %0 : tensor<1x2x3x4xf32>

// CHECK-LABEL:reverse_v2
// CHECK:  "tfl.reverse_v2"(%arg0, %arg1) : (tensor<1x2x3x4xf32>, tensor<1xi32>) -> tensor<1x2x3x4xf32>
// CHECK:  return
}

  %0 = "tf.BatchMatMulV2"(%lhs, %rhs) : (tensor<4x5x1x?x3xf32>, tensor<1x1x3x5xf32>) -> tensor<4x5x1x?x5xf32>
  func.return %0 : tensor<4x5x1x?x5xf32>
}

// -----

func.func @testBatchMatMulV2InvalidPartiallyKnownMatmulDim(%lhs: tensor<4x5x1x?x3xf32>, %rhs: tensor<1x1x3x5xf32>) -> (tensor<4x5x1x?x3xf32>) {