%2 = "mhlo.dot_general"(%arg2, %arg3) {
    dot_dimension_numbers = #mhlo.dot<
      lhs_contracting_dimensions = [2],
      rhs_contracting_dimensions = [0]
  >} : (tensor<3x1x512xf32>, tensor<512x13xf32>) -> tensor<3x1x13xf32>
  %r = "mhlo.concatenate"(%0, %1, %2) <{dimension = 0 : i64}> : (tensor<1x1x13xf32>, tensor<2x1x13xf32>, tensor<3x1x13xf32>) -> tensor<6x1x13xf32>

  %4 = "quantfork.stats"(%3) {
    layerStats = dense<[0.0, 4.0]> : tensor<2xf32>
  } : (tensor<?x1x1x3xf32>) -> tensor<?x1x1x3xf32>
  %5 = "tfl.sqrt"(%4) : (tensor<?x1x1x3xf32>) -> tensor<?x1x1x3xf32>
  %6 = "quantfork.stats"(%5) {
    layerStats = dense<[0.0, 2.0]> : tensor<2xf32>
  } : (tensor<?x1x1x3xf32>) -> tensor<?x1x1x3xf32>

  %prelu = "tfl.prelu"(%arg0, %cst) : (tensor<1x10x10x3xf32>, tensor<1x1x3xf32>) -> tensor<1x10x10x3xf32>
  func.return %prelu : tensor<1x10x10x3xf32>

// CHECK: %[[cst:.*]] = arith.constant dense<[{{\[}}[1.66394591, 3.61694336, 2.0382936]]]> : tensor<1x1x3xf32>

    %11 = stablehlo.convert %3 : (tensor<1x1x3xi32>) -> tensor<1x1x3xf32>
    %12 = stablehlo.broadcast_in_dim %11, dims = [0, 1, 2] : (tensor<1x1x3xf32>) -> tensor<1x4x3xf32>  // Optional
    %13 = stablehlo.subtract %10, %12 : tensor<1x4x3xf32>  // Precalculated zp_neg.
    %14 = stablehlo.broadcast_in_dim %4, dims = [0, 1, 2] : (tensor<1x1x3xf32>) -> tensor<1x4x3xf32>  // Optional

// CHECK-NEXT: return %[[ES]] : tensor<3x2x3xf32>
}

// CHECK-LABEL: my_leaky_relu
func.func @my_leaky_relu(%arg0: tensor<2x3xf32>) -> tensor<3x2x3xf32> {
  %0 = "tf.MyLeakyRelu"(%arg0) {alpha=3.0 : f32} : (tensor<2x3xf32>) -> tensor<3x2x3xf32>
  func.return %0 : tensor<3x2x3xf32>

// CHECK-LABEL: QuantizeReshapeOp
func.func @QuantizeReshapeOp(%arg0: tensor<1x1x3xf32>) -> (tensor<1x3xf32>) {
  %1 = "quantfork.stats"(%arg0) {layerStats = dense<[-1.0, 1.0]> : tensor<2xf32>} : (tensor<1x1x3xf32>) -> tensor<1x1x3xf32>
  %2 = "tfl.pseudo_const"() {value = dense<[-1, 3]> : tensor<2xi32>} : () -> tensor<2xi32>
  %3 = "tfl.reshape"(%1, %2) : (tensor<1x1x3xf32>, tensor<2xi32>) -> tensor<1x3xf32>

  // CHECK: return %[[v3]] : tensor<7x11x13xf32>
}

func.func @einsum_ellipsis_with_broadcast(%arg0: tensor<5x4x3xf32>, %arg1: tensor<3x2x1xf32>) -> tensor<4x2x5xf32> {
  %0 = "tf.Einsum"(%arg0, %arg1) {device = "", equation = "...ij,j...->i..."} : (tensor<5x4x3xf32>, tensor<3x2x1xf32>) -> tensor<4x2x5xf32>
  func.return %0 : tensor<4x2x5xf32>

  constexpr absl::string_view
      kModuleXlaCallModuleOpWithDefaultQuantizationMethod = R"mlir(
    func.func @xla_call_module_default_quantization_method(%arg0: tensor<1x1x3xf32>, %arg1: tensor<3x4xf32>) -> tensor<1x1x4xf32> {

    %4:4 = "tf.CustomAggregator"(%3) {calibration_method = 1 : i32, id = "1", max_percentile = 0.000000e+00 : f32, min_percentile = 0.000000e+00 : f32, num_bins = 0 : i32} : (tensor<3x11xf32>) -> (tensor<3x11xf32>, tensor<f32>, tensor<f32>, tensor<*xi64>)
    %5 = stablehlo.add %4#0, %1 : tensor<3x11xf32>
    %6 = stablehlo.multiply %5, %1 : tensor<3x11xf32>

func.func @ensureBiasForConv2d(%arg0: tensor<128x32x32x3xf32>, %arg1: tensor<32x1x1x3xf32>) -> tensor<128x32x32x32xf32> {
  %cst = "tfl.no_value"() {value = unit} : () -> none
  %0 = "tfl.conv_2d"(%arg0, %arg1, %cst) {dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "NONE", padding = "VALID", stride_h = 1 : i32, stride_w = 1 : i32} : (tensor<128x32x32x3xf32>, tensor<32x1x1x3xf32>, none) -> tensor<128x32x32x32xf32>