^bb0(%arg2: tensor<*xi32>, %arg3: tensor<*xf32>):
    %1:2 = func.call @WhileOp_body(%arg2, %arg3) : (tensor<*xi32>, tensor<*xf32>) -> (tensor<*xi32>, tensor<*xf32>)
    "tfl.yield"(%1#0, %1#1) : (tensor<*xi32>, tensor<*xf32>) -> ()
  }) : (tensor<i32>, tensor<1xf32>) -> (tensor<i32>, tensor<1xf32>) loc("WhileOp")
  func.return %0#1 : tensor<1xf32>

    %2 = "tf.Reshape"(%1, %cst_0) : (tensor<1x10xf32>, tensor<2xi32>) -> tensor<?x10xf32> loc(fused["Reshape:", "model/reshape"])
    %3 = "tf.AddV2"(%2, %cst) : (tensor<?x10xf32>, tensor<10xf32>) -> tensor<?x10xf32> loc(fused["AddV2:", "model/add"])
    func.return %3 : tensor<?x10xf32>
  }
}

// CHECK-LABEL: func @matmul_with_reshape_disable_quantization
// CHECK: "tf.PartitionedCall"

  %m0 = mhlo.constant dense<0> : tensor<i32>
  %t0 = "tf.Const"() { value = dense<0> : tensor<1xi32> } : () -> tensor<1xi32>
  %m1 = mhlo.constant dense<1> : tensor<i32>
  %t1 = "tf.Const"() { value = dense<1> : tensor<1xi32> } : () -> tensor<1xi32>
  %m2 = mhlo.constant dense<1> : tensor<i32>
  %t2 = "tf.Const"() { value = dense<1> : tensor<1xi32> } : () -> tensor<1xi32>
  %m3 = mhlo.constant dense<1> : tensor<i32>

func.func @main(%arg0: tensor<10xi32>, %arg1: tensor<10xi32>) -> tensor<10xi32>
attributes {tf.entry_function = {inputs = "input0,input1", outputs = "Add"}} {
  %graph = tf_executor.graph {
    %2:2 = tf_executor.island wraps "tf.Add"(%arg0, %arg1) {T = "tfdtype$DT_INT32", device = ""} : (tensor<10xi32>, tensor<10xi32>) -> tensor<10xi32> loc("Add")
    tf_executor.fetch %2 : tensor<10xi32>
  }
  func.return %graph : tensor<10xi32>
}

  %5 = "stablehlo.concatenate"(%4, %1) {dimension = 0 : i64} : (tensor<?xf32>,  tensor<?xf32>) -> tensor<?xf32>
  %6 = "quantfork.stats"(%5) {layerStats = dense<[-1.5726943, 4.6875381]> : tensor<2xf32>} : (tensor<?xf32>) -> tensor<?xf32>
  func.return  %3, %6 : tensor<?xf32>, tensor<?xf32>
}

// -----

  func.return %0 : tensor<? x ? x ? x f32>
}

          [%u0#0, %u0#1] as %u : tensor<32xf32>)
          {n = 2 : i32, devices = {CORE_0 = ["/CPU:0", "/GPU:1"]}} {
        %read0 = "tf.ReadVariableOp"(%r0) : (tensor<*x!tf_type.resource<tensor<32xf32>>>) -> tensor<32xf32>
        // expected-remark@above {{ID: 1}}
        "tf.AssignVariableOp"(%r1, %u) : (tensor<*x!tf_type.resource<tensor<32xf32>>>, tensor<32xf32>) -> ()
        // expected-remark@above {{ID: 2}}

// CHECK-LABEL: func @init
func.func @init(%arg0: tensor<4xf32>) -> tensor<7xf32> {
    %0 = builtin.unrealized_conversion_cast to tensor<7xf32>
    return %0 : tensor<7xf32>
}

// CHECK-LABEL: func @next
func.func @next(%arg0: tensor<7xf32>, %arg1: tensor<3xf32>) -> tensor<6xf32> {
    %0 = builtin.unrealized_conversion_cast to tensor<6xf32>
    return %0 : tensor<6xf32>
}

// CHECK-LABEL: func @finalize

  %1 = "tf.Mul"(%arg1, %arg1) : (tensor<2xf32>, tensor<2xf32>) -> tensor<2xf32>
  %2 = "tf.Div"(%arg1, %arg1) : (tensor<2xf32>, tensor<2xf32>) -> tensor<2xf32>

  // CHECK: "tf.If"{{.+}}else_branch = @tf.IfRegion_else{{.+}}then_branch = @tf.IfRegion_then
  %3 = "tf.IfRegion"(%arg0) ({
     %4 = "tf.Select"(%arg0, %0, %1) : (tensor<i1>, tensor<2xf32>, tensor<2xf32>) -> tensor<2xf32>
     "tf.Yield"(%4) : (tensor<2xf32>) -> ()

  func.func @f(%arg0: tensor<!tf_type.resource<tensor<?xf32>>> {tf_saved_model.bound_input = @v})
  -> (tensor<!tf_type.resource<tensor<?xf32>>> {tf_saved_model.index_path = []})
  attributes {tf_saved_model.exported_names = ["f"]} {
    // CHECK: return
    return %arg0 : tensor<!tf_type.resource<tensor<?xf32>>>
  }