} : (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>) -> ()

// PerChannel-DAG: %[[scale:.*]] = "tf.Const"() <{value = dense<{{[0-9\.Ee\+\-]+}}> : tensor<2xf32>}> : () -> tensor<2xf32>
// PerChannel-DAG: %[[zp:.*]] = "tf.Const"() <{value = dense<{{[0-9]+}}> : tensor<2xi32>}> : () -> tensor<2xi32>
// PerChannel: %[[out_1:.*]] = "tf.PartitionedCall"(%arg0, %[[q_w]], %[[scale]], %[[zp]]) <{config = "", config_proto = "", executor_type = "",

    %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>

// CHECK: %[[reshape_i8:.*]] = "tf.Reshape"(%[[maxpool_i8]]
// CHECK-SAME: (tensor<*xf32>, tensor<2xi32>) -> tensor<*xf32>

// -----

// CHECK-LABEL: standalone_avgpool_test
func.func @standalone_avgpool_test(%arg0: tensor<*xf32>) -> tensor<*xf32> {
  %cst = arith.constant dense<[-1, 144]> : tensor<2xi32>
  %0 = "quantfork.qcast"(%arg0) : (tensor<*xf32>) -> tensor<*x!quant.uniform<i8:f32, 5.000000e-02:-10>>

  %outputs_0 = "tf.Const"() {value = dense<224> : tensor<2xi32>} : () -> tensor<2xi32>
  %outputs_2 = "tf.Const"() {value = dense<0> : tensor<i32>} : () -> tensor<i32>
  %outputs_4 = "tf.Const"() {value = dense<1> : tensor<1xi32>} : () -> tensor<1xi32>
  %outputs_6 = "tf.Const"() {value = dense<0> : tensor<1xi32>} : () -> tensor<1xi32>

  // Reduce over spatial dimensions (NCHW)
  %1 = "tf.Const"() {value = dense<[2, 3]> : tensor<2xi32>} : () -> tensor<2xi32>

  // Transpose input: NHWC -> NCHW
  %2 = "tf.Const"() {value = dense<[0, 3, 1, 2]> : tensor<4xi32>} : () -> tensor<4xi32>
  %3 = "tf.Transpose"(%arg0, %2) : (tensor<?x224x224x3xf32>, tensor<4xi32>) -> tensor<?x3x224x224xf32>

  // Pad spatial dimensions.

  %scales_0 = "tf.Const"() { value = dense<[1.0, 2.0]> : tensor<2xf32> } : () -> tensor<2xf32>
  %zps_0 = "tf.Const"() { value = dense<[3, 4]> : tensor<2xi32> } : () -> tensor<2xi32>
  %scales_1 = "tf.Const"() { value = dense<[3.0, 4.0]> : tensor<2xf32> } : () -> tensor<2xf32>
  %zps_1 = "tf.Const"() { value = dense<[5, 6]> : tensor<2xi32> } : () -> tensor<2xi32>

// RUN: json_to_flatbuffer %p/test_schema.fbs %s | flatbuffer_translate --tflite-flatbuffer-to-mlir -o - | FileCheck %s

// CHECK: %0 = "tfl.pseudo_const"() <{value = dense<2> : tensor<2xi32>}> : () -> tensor<2xi32>
// CHECK: %1 = "tfl.reshape"(%arg0, %0) : (tensor<1x4xf32>, tensor<2xi32>) -> tensor<2x2xf32>

{
  "version": 3,
  "operator_codes": [
    {
      "builtin_code": "RESHAPE"
    }
  ],
  "subgraphs": [
    {
      "tensors": [