// CHECK: %{{.*}} = "tfl.reshape"(%{{.*}}, %[[cst]]) : (tensor<4xf32>, tensor<2xi32>) -> tensor<2x2xf32>
  %cst = arith.constant dense<[2, 2]> : tensor<2xi32>
  %0 = "tfl.pseudo_const" () {value = dense<1.0> : tensor<4xf32>} : () -> tensor<4xf32> loc("Const")
  %1 = "tfl.reshape" (%0, %cst) : (tensor<4xf32>, tensor<2xi32>) -> tensor<2x2xf32> loc("reshape")

// CHECK-SAME: %[[b_scale]], %[[w_zp]], %[[out_scale]], %[[out_zp]])
// CHECK-SAME: f = @quantized_conv2d_with_bias_and_relu6_fn_0
// CHECK-SAME: (tensor<1x2x2x3xi8>, tensor<2x2x3x2xi8>, tensor<2xi32>, tensor<f32>, tensor<i32>, tensor<2xf32>, tensor<2xi32>, tensor<2xf32>, tensor<2xi32>, tensor<f32>, tensor<i32>) -> tensor<*xi8>
// CHECK: %[[dequantize:.*]] = "tf.PartitionedCall"(%[[conv_quant]], %[[out_scale]], %[[out_zp]])
// CHECK-SAME: f = @dequantize_i8

      } : (tensor<!tf_type.resource<tensor<2xi32>>>) -> tensor<*xi32>
    return %1 : tensor<*xi32>
  }

  func.func private @called_by_batch_func_assign(%arg: tensor<*x!tf_type.resource>) -> tensor<?xi32> {
    %0 = "tf.Const"() {value = dense<4> : tensor<2xi32>} : () -> tensor<2xi32>
    "tf.AssignVariableOp"(%arg, %0) : (tensor<*x!tf_type.resource>, tensor<2xi32>) -> ()

  %cst = arith.constant dense<[2, 2]> : tensor<2xi32>
  %cst_0 = arith.constant dense<4> : tensor<2x2xi32>
  %0 = "tf.SpaceToBatchND"(%arg0, %cst, %cst_0) : (tensor<1x128x128x3xf32>, tensor<2xi32>, tensor<2x2xi32>) -> tensor<4x68x68x3xf32>
  %1 = "tf.SpaceToBatchND"(%arg0, %cst, %cst_0) : (tensor<1x128x128x3xf32>, tensor<2xi32>, tensor<2x2xi32>) -> tensor<4x68x68x3xf32>

    %2 = "tf.OpC"(%1) {} : (tensor<2xi32>) -> (tensor<2xi32>)
    %3 = "tf.OpB"(%cst_0, %2) { mini_batch_in_csr = ""} : (tensor<1x!tf_type.string>, tensor<2xi32>) -> (tensor<2xi32>)
    %4 = "tf.OpD"(%3) {} : (tensor<2xi32>) -> (tensor<2xi32>)
    tf_device.return %4 : tensor<2xi32>
  }) {device = "/job:localhost/replica:0/task:0/device:CPU:0"} : () -> (tensor<2xi32>)
  %0:2 = "tf_device.launch"() ({

// CHECK-LABEL: func @strip_simple(
// CHECK-NOT: tf._noinline
func.func @strip_simple() -> tensor<2xi32> attributes {tf._noinline = true} {
  // CHECK-NEXT: %[[CST:.*]] = "tf.Const"
  %cst = "tf.Const"() { value = dense<2> : tensor<2xi32> } : () -> tensor<2xi32>
  func.return %cst : tensor<2xi32>

      %0  = "tf.Const"() {value = dense<[56, 904]> : tensor<2xi32>} : () -> tensor<2xi32>
      // CHECK: %{{.*}} = stablehlo.custom_call @tf.ResizeBilinear(%arg0, %{{.*}}) {align_corners = false, device = "", half_pixel_centers = true} : (tensor<1x32x32x128xf32>, tensor<2xi32>) -> tensor<1x64x64x128xf32>

# NONE-SAME:  control_outputs = ""
# NONE-SAME:  inputs = "input0,input1"
# NONE-SAME:  outputs = "Add"
# NONE:           %[[add:.*]], %[[add_control:.*]] = tf_executor.island wraps "tf.Add"(%[[ARG_0]], %[[ARG_1]])
# NONE:           fetch %[[add]]

# UNKNOWN-LABEL: func @main

func.func @modified(%arg0: tensor<1x224x224x3xf32>) -> tensor<1x401408xf32> attributes {tf.entry_function = {control_outputs = "", inputs = "input", outputs = "output"}} {
  %cst = arith.constant dense<[1, 401408]> : tensor<2xi32>
  %0 = "tfl.quantize"(%arg0) {qtype = tensor<1x224x224x3x!quant.uniform<i8:f32, 7.812500e-03>>} : (tensor<1x224x224x3xf32>) -> tensor<1x224x224x3x!quant.uniform<i8:f32, 7.812500e-03>>

func.func @foldDivI32() -> tensor<2xi32> {
  %0 = stablehlo.constant dense<[9, 3]> : tensor<2xi32>
  %1 = stablehlo.constant dense<[4, 6]> : tensor<2xi32>
  %2 = stablehlo.divide %0, %1 : tensor<2xi32>
  return %2 : tensor<2xi32>
}

// CHECK-LABEL: foldDivI32
// CHECK: stablehlo.constant dense<[2, 0]> : tensor<2xi32>

// -----

func.func @divideToMulReciprocalSplat(%arg0: tensor<2xf32>) -> tensor<2xf32> {