// CHECK: @init_func_restore_op
  // CHECK-SAME: tf_saved_model.initializer_type = "restore_op"
  // CHECK-NOT: "tf.AssignVariableOp"
  // CHECK: %[[CST:.*]] = "tf.Const"()
  // CHECK-NEXT: %[[IDENTITY:.*]] = "tf.Identity"(%[[CST]])
  // CHECK-NEXT: %[[VAR:.*]] = "tf.VarHandleOp"() <{{{.*shared_name = "var_1".*}}}>
  // CHECK-NEXT: "tf.AssignVariableOp"(%[[VAR]], %[[IDENTITY]])
}

// -----

  %cst = "tfl.no_value"() {value = unit} : () -> none

    %cst = arith.constant dense<0.000000e+00> : tensor<5xf32>
    func.return %cst : tensor<5xf32>
  }

  %cst = arith.constant dense<[2, 100]> : tensor<2xi64>
  // CHECK: tac.cost = 4.000000e+01
  %0 = "tfl.concatenation"(%arg0, %arg1) {axis = 0 : i32, fused_activation_function = "NONE", tac.device = "GPU"} : (tensor<100xf32>, tensor<100xf32>) -> tensor<200xf32>
  // CHECK: tac.cost = 4.040000e+01
  %1 = "tfl.reshape"(%0, %cst) {tac.device = "GPU"} : (tensor<200xf32>, tensor<2xi64>) -> tensor<2x100xf32>

// CHECK-NOT: tf.ReadVariableOp
  %0 = "tf.ReadVariableOp"(%arg0) : (tensor<*x!tf_type.resource<tensor<1xf32>>>) -> tensor<1xf32>
  %cst = "tf.Const"() <{value = dense<0.000000e+00> : tensor<f32>}> : () -> tensor<f32>
  %cst_0 = "tf.Const"() <{value = dense<1> : tensor<1xi64>}> : () -> tensor<1xi64>
  %fill = "tf.Fill"(%cst_0, %cst) : (tensor<1xi64>, tensor<f32>) -> tensor<1xf32>
  tf_device.replicate([%0, %fill] as %arg_r0: tensor<1xf32>) {n = 2 : i32} {

    %cst = "tf.Const"() {value = dense<2> : tensor<960x960x3x512xi8>} : () -> tensor<960x960x3x512xi8>
    %cst_0 = "tf.Const"() {value = dense<0.00117647066> : tensor<f32>} : () -> tensor<f32>

    tf_device.return %2 : tensor<i32>
  }) {cluster_attr = "cluster_attr"} : () -> tensor<i32>
  %cst = "tf.Const"() {value = dense<5> : tensor<i32>} : () -> tensor<i32>
  %1 = "tf.Add"(%0, %cst) : (tensor<i32>, tensor<i32>) -> tensor<i32>
  func.return %1 : tensor<i32>
}

func.func @stateful_pcall_func(%arg0: tensor<i32>) -> tensor<i32> {

  %2:2 = "tf.Split"(%cst, %arg0) {device = "/CPU:0"} : (tensor<i32>,  tensor<1x4xf32>) -> (tensor<1x2xf32>, tensor<1x2xf32>)
  %3:2 = "tf.Split"(%cst, %2#0) {device = "/CPU:0"} : (tensor<i32>,  tensor<1x2xf32>) -> (tensor<1x1xf32>, tensor<1x1xf32>)
  %4:2 = "tf.Split"(%cst, %2#1) {device = "/CPU:0"} : (tensor<i32>,  tensor<1x2xf32>) -> (tensor<1x1xf32>, tensor<1x1xf32>)

// RUN: tf-quant-opt %s -split-input-file -quant-prepare-quantize | FileCheck %s

module {
  func.func @same_scale_test(%arg0: tensor<*xf32>) -> tensor<*xf32> {
    %cst = arith.constant dense<[-1, 144]> : tensor<2xi32>
    %cst_1 = arith.constant dense<1.0> : tensor<144x10xf32>
    %cst_2 = arith.constant dense<0.1> : tensor<10xf32>
    %0 = "quantfork.qcast"(%arg0) : (tensor<*xf32>) -> tensor<*x!quant.uniform<i8:f32, 0.05:-10>>

// the quantization parameters as a TypeAttr and "tfl.dequantize" op used to
// convert the output type to the next op. Here are the transformations:
//
// input   min cst       max cst          input   min cst       max cst
//  \       |             |                \       |             |
//   \  (tf.Identity) (tf.Identity)   =>    \  (tf.Identity) (tf.Identity)
//    \     |             |                  \     |             |