func.func @conv_fn(%arg0: tensor<1x3x3x4xf32>) -> tensor<1x3x3x4xf32> {
  %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.return %1: tensor<1x3x3x4xf32>
}

// fallback lowering is preferred for static shaped operands when available.

// CHECK-LABEL: batchmatmulv2
func.func @batchmatmulv2(%arg0: tensor<1x4x2xf32>, %arg1: tensor<3x2x4xf32>) -> tensor<3x4x4xf32> {
  // NO_FALLBACK: mhlo.dynamic_broadcast_in_dim
  // NO_FALLBACK: mhlo.dot_general

  // SUPPORTED_FALLBACK_DEVICE: mhlo.reduce
  // SUPPORTED_FALLBACK_DEVICE: mhlo.dot_general

      func.func @main() -> (tensor<32x64xf32> {mhlo.sharding = "\08\01\1A\01\01\22\01\00"}) {
        %cst = "tf.Const"() {value = dense<[524170, 523952]> : tensor<2xi32>} : () -> tensor<2xi32>
        %cst_0 = "tf.Const"() {value = dense<[32, 64]> : tensor<2xi32>} : () -> tensor<2xi32>
        %0 = "tf.StatelessRandomNormal"(%cst_0, %cst) : (tensor<2xi32>, tensor<2xi32>) -> tensor<32x64xf32>
        return %0 : tensor<32x64xf32>
    }

    %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>

    >,
    precision_config = [#mhlo<precision DEFAULT>, #mhlo<precision DEFAULT>]
  } : (tensor<3x2x6x5x1xf32>, tensor<3x2x4x6xf32>) -> tensor<3x5x1x4xf32>
  func.return %0 : tensor<3x5x1x4xf32>

// CHECK-LABEL:   convert_dot_general
// CHECK:         %[[TRANSPOSED_0:.*]] = "tfl.transpose"
// CHECK:         %[[TRANSPOSED_1:.*]] = "tfl.transpose"
// CHECK-NEXT:    %[[RESHAPED_0:.*]] = mhlo.reshape %[[TRANSPOSED_0]]

func.func @add_with_activation_transpose_permutation_mismatch(
      %arg0: tensor<1x2x3x4xf32>) -> tensor<1x3x2x4xf32> {
  %0 = stablehlo.constant dense<2.000000e+00> : tensor<1x3x2x4xf32>
  %1 = stablehlo.transpose %arg0, dims = [0, 2, 1, 3] : (tensor<1x2x3x4xf32>) -> tensor<1x3x2x4xf32>
  %2 = stablehlo.add %1, %0 : tensor<1x3x2x4xf32>
  return %2 : tensor<1x3x2x4xf32>
}

}

func.func @depthwiseConv2D(tensor<256x32x32x3xf32>, tensor<3x3x3x4xf32>, tensor<256x3x32x32xf32>) -> (tensor<256x30x30x12xf32>, tensor<256x12x30x30xf32>, tensor<256x30x30x12xf32>, tensor<256x30x30x12xf32>) {
^bb0(%arg0: tensor<256x32x32x3xf32>, %arg1: tensor<3x3x3x4xf32>, %arg2: tensor<256x3x32x32xf32>) :
   // OK

  %fq = "tf.FakeQuantWithMinMaxVarsPerChannel"(%in, %mini, %maxi) {num_bits = 4, narrow_range = true} : (tensor<3x3x3x4xf32>, tensor<4xf32>, tensor<4xf32>) -> tensor<3x3x3x4xf32>
  %rst = "tf.Conv2D"(%arg, %fq) {T = "tfdtype$DT_FLOAT", data_format = "NHWC", dilations = [1, 2, 3, 1], padding = "SAME", strides = [1, 4, 5, 1]} : (tensor<256x32x32x3xf32>, tensor<3x3x3x4xf32>) -> tensor<256x8x7x4xf32>
  func.return %rst : tensor<256x8x7x4xf32>

// 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
}

}

// -----

// 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