%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

  %0 = "mhlo.broadcast_in_dim"(%arg1) <{broadcast_dimensions = dense<[0, 1]> : tensor<2xi64>}> : (tensor<1x1xi32>) -> tensor<1x100xi32>
  %1 = "mhlo.select"(%arg0, %0, %arg2) : (tensor<i1>, tensor<1x100xi32>, tensor<1x100xi32>) -> tensor<1x100xi32>
  func.return %1 : tensor<1x100xi32>
}

// CHECK-LABEL:   func @selectv2_broadcasted_condition(

      return %0 : tensor<1x1x4xf32>
    }
  )mlir";

  const OwningOpRef<ModuleOp> module_op =
      ParseModuleOpString(kXlaCallModuleOpWithQuantizationMethodAttr);
  ASSERT_TRUE(module_op);

func.func @concatConstantTensorsMiddleDim() -> tensor<1x4x3xi32> {
  %cst_0 = arith.constant dense<0> : tensor<1x2x3xi32>
  %cst_1 = arith.constant dense<1> : tensor<1x2x3xi32>
  %0 = "tfl.concatenation"(%cst_0, %cst_1) {axis = 1 : i32, fused_activation_function = "NONE"} : (tensor<1x2x3xi32>, tensor<1x2x3xi32>) -> tensor<1x4x3xi32>
  func.return %0 : tensor<1x4x3xi32>

  %0 = "tfl.fully_connected" (%arg0, %arg1, %arg2) {fused_activation_function = "NONE", keep_num_dims = false, weights_format = "DEFAULT"} : (tensor<1x256xf32>, tensor<128x256xf32>, tensor<128xf32>) -> tensor<1x128xf32>
  %1 = "tfl.relu"(%0) : (tensor<1x128xf32>) -> tensor<1x128xf32>
  func.return %1 : tensor<1x128xf32>

  // CHECK: %[[RES:[0-9].*]] = "tfl.fully_connected"

module {
func.func @tfInplaceUpdate(%arg0: tensor<2x1x2xf32>) -> tensor<2x1x2xf32> {
  %1 = arith.constant dense<1> : tensor<1xi32>
  %2 = arith.constant dense<2.0> : tensor<1x1x2xf32>
  %3 = "tf.InplaceUpdate"(%arg0, %1, %2) {device = ""}
    : (tensor<2x1x2xf32>, tensor<1xi32>, tensor<1x1x2xf32>) -> tensor<2x1x2xf32>
  func.return %3 : tensor<2x1x2xf32>
}
}

//CHECK: module attributes
//CHECK-SAME: keep_stablehlo_constant = "true"

module {
func.func @tfInplaceUpdate(%arg0: tensor<2x1x2xf32>) -> tensor<2x1x2xf32> {
  %1 = arith.constant dense<1> : tensor<1xi32>
  %2 = arith.constant dense<2.0> : tensor<1x1x2xf32>
  %3 = "tf.InplaceUpdate"(%arg0, %1, %2) {device = ""}
    : (tensor<2x1x2xf32>, tensor<1xi32>, tensor<1x1x2xf32>) -> tensor<2x1x2xf32>
  func.return %3 : tensor<2x1x2xf32>
}
}

//CHECK: module {

// 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-LABEL: QuantizeWithoutNorm
func.func @QuantizeWithoutNorm(%arg0: tensor<1x1x5xf32>) -> tensor<*xf32> attributes {tf.entry_function = {inputs = "input0", outputs = "output24"}} {
  %none = "tfl.no_value"() {value = unit} : () -> none
  %input = "quantfork.stats"(%arg0) {layerStats = dense<[-1.2, 1.5]> : tensor<2xf32>} : (tensor<1x1x5xf32>) -> tensor<1x1x5xf32>

  %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>