%10 = stablehlo.convert %3 : (tensor<1x1x3xi32>) -> tensor<1x1x3xf32>
    %11 = stablehlo.broadcast_in_dim %10, dims = [0, 1, 2] : (tensor<1x1x3xf32>) -> tensor<1x4x3xf32>  // Optional
    %12 = stablehlo.subtract %9, %11 : tensor<1x4x3xf32>  // Precalculated zp_neg.
    %13 = stablehlo.broadcast_in_dim %4, dims = [0, 1, 2] : (tensor<1x1x3xf32>) -> tensor<1x4x3xf32>  // Optional
    %14 = stablehlo.multiply %12, %13 : tensor<1x4x3xf32>  // s1 * s2

// 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>

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

  OwningOpRef<ModuleOp> module_op =
      ParseModuleOpString(kModuleXlaCallModuleOpWithDefaultQuantizationMethod);
  ASSERT_TRUE(module_op);

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

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

  %50 = "tfl.read_variable"(%5) : (tensor<!tf_type.resource>) -> tensor<1x2x3xf32>
  %51 = "quantfork.stats"(%50) {layerStats = dense<[0.0, 1.0]> : tensor<2xf32>} : (tensor<1x2x3xf32>) -> tensor<1x2x3xf32>
  %52 = "tfl.concatenation"(%51, %0) {axis = 1 : i32, fused_activation_function = "NONE"} : (tensor<1x2x3xf32>, tensor<1x2x3xf32>) -> tensor<1x4x3xf32>

  %cst_0 = arith.constant dense<0> : tensor<1x2x3xi32>
  %cst_1 = arith.constant dense<1> : tensor<1x2x3xi32>
  %0 = "tfl.concatenation"(%cst_0, %cst_1) {axis = 2 : i32, fused_activation_function = "NONE"} : (tensor<1x2x3xi32>, tensor<1x2x3xi32>) -> tensor<1x2x6xi32>
  func.return %0 : tensor<1x2x6xi32>

  // CHECK: %[[CST:.*]] = arith.constant dense<[{{\[}}{{\[}}0, 0, 0, 1, 1, 1], {{\[}}0, 0, 0, 1, 1, 1]]]> : tensor<1x2x6xi32>

  // CHECK-SAME: ({{%.+}}: tensor<1x2x3xf32>)
  // CHECK-SAME: -> (tensor<1x8x3xf32>, tensor<1x8x3xf32>)
  func.func @while_shape_invariant_different_dims(%arg0: tensor<1x2x3xf32>) -> (tensor<1x8x3xf32>, tensor<1x8x3xf32>) {
    // CHECK: "tf.While"
    // CHECK-SAME: (tensor<1x2x3xf32>)
    // CHECK-SAME: -> tensor<1x8x3xf32>

// CHECK:}

func.func @reshape(%arg0 : tensor<1x128xi32>) -> tensor<4x32x1xi32>{
  %0 = "vhlo.reshape_v1"(%arg0) : (tensor<1x128xi32>) -> tensor<4x32x1xi32>
  func.return %0 : tensor<4x32x1xi32>
}

//CHECK:func.func private @reshape(%arg0: tensor<1x128xi32>) -> tensor<4x32x1xi32> {
//CHECK-NEXT: %0 = "vhlo.reshape_v1"(%arg0) : (tensor<1x128xi32>) -> tensor<4x32x1xi32>
//CHECK-NEXT: return %0 : tensor<4x32x1xi32>

    %10 = "tf.AddV2"(%9, %cst_2) {device = ""} : (tensor<2x1x3xi32>, tensor<3xi32>) -> tensor<2x1x3xi32>
    %11 = "tf.Cast"(%10) {Truncate = false, device = ""} : (tensor<2x1x3xi32>) -> tensor<2x1x3xf32>
    %12 = "tf.Mul"(%11, %cst) {device = ""} : (tensor<2x1x3xf32>, tensor<f32>) -> tensor<2x1x3xf32>
    %13 = "tf.Identity"(%12) {device = ""} : (tensor<2x1x3xf32>) -> tensor<2x1x3xf32>

  %0 = "tfl.custom"(%arg0) {custom_code = "stablehlo.broadcast_in_dim", custom_option = #tfl<const_bytes : "0x62726F6164636173745F64696D656E73696F6E73000201020119010101072C022401">} : (tensor<1x2xi32>) -> tensor<1x2x2xi32>
  func.return %0 : tensor<1x2x2xi32>
  }
}

// CHECK:      module {
// CHECK-NEXT:  func @main(%arg0: tensor<1x2xi32>) -> tensor<1x2x2xi32> {