// CHECK: shape
// CHECK: dim
// CHECK: size: 10

func.func @main() {
  tf_executor.graph {
    %0:2 = tf_executor.island wraps "tf.Const"() {dtype = "tfdtype$DT_INT32", value = dense<0> : tensor<10xi32>} : () -> tensor<10xi32>
    %1:2 = tf_executor.island wraps "tf.Const"() {dtype = "tfdtype$DT_INT32", value = dense<0> : tensor<i32>} : () -> tensor<i32>

// RUN: tf-mlir-translate -mlir-to-graphdef %s -o - | FileCheck %s

func.func @main(%arg0: tensor<10xi32>) -> tensor<10xi32>
attributes {tf.entry_function = {inputs = "input0", outputs = "output0"}} {
  %graph = tf_executor.graph {
    tf_executor.fetch %arg0 : tensor<10xi32>
  }
  func.return %graph : tensor<10xi32>
}

// CHECK:      node {
// CHECK-NEXT:   name: "input0"
// CHECK-NEXT:   op: "_Arg"
// CHECK:          key: "T"

  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>>>
  }

  func.func @init() attributes {tf_saved_model.exported_names = ["__tf_saved_model_session_initializer"], tf_saved_model.initializer_type = "restore_op"} {
    "tf.Const"() {value = dense<[1.0]> : tensor<1xf32> } : () -> tensor<1xf32>
    func.return
  }
}

// -----

module attributes {tf_saved_model.semantics, tf_saved_model.under_construction} {
  // Test case: Variable removal.
  // CHECK: func @init()

# CHECK-LABEL: func @main
# CHECK-SAME:  ([[VAL_0:%.*]]: tensor<1x8x8x2xi32>) -> (tensor<1x8x8x2xi32>, tensor<i32>, tensor<10xi32>)
# CHECK-SAME:  control_outputs = ""
# CHECK-SAME:  inputs = "unranked"
# CHECK-SAME:  outputs = "unranked,static,static_10"
# CHECK-DAG:         [[VAL_2:%.*]] = arith.constant dense<0> : tensor<10xi32>
# CHECK-DAG:         [[VAL_1:%.*]] = arith.constant dense<0> : tensor<i32>

    # CHECK: "tf_saved_model.global_tensor"() <{is_mutable, {{.*}} type = tensor<*xf32>, value = dense<0.000000e+00> : tensor<1xf32>}> {tf_saved_model.exported_names = ["v0"]} : () -> ()
    # CHECK: "tf_saved_model.global_tensor"() <{is_mutable, {{.*}} type = tensor<?xf32>, value = dense<[0.000000e+00, 1.000000e+00]> : tensor<2xf32>}> {tf_saved_model.exported_names = ["v1"]} : () -> ()
    self.v0 = tf.Variable([0.], shape=tf.TensorShape(None))

    %4 = "quantfork.stats"(%2) {layerStats = dense<[-2795.36523, 4609.57373]> : tensor<2xf32>} : (tensor<*xf32>) -> tensor<*xf32>
    %5 = "tf.BiasAdd"(%4, %3) {data_format = "NHWC", device = ""} : (tensor<*xf32>, tensor<2xf32>) -> tensor<*xf32>
    %6 = "quantfork.stats"(%5) {layerStats = dense<[-2788.31055, 4616.62842]> : tensor<2xf32>} : (tensor<*xf32>) -> tensor<*xf32>

        var_handle_op.getContext(), var_handle_op.getSharedName()));
    var_handle_op->setLoc(loc);

    // Ex) If VarHandleOp's type is tensor<!tf_type.resource<tensor<1xf32>>>,
    // then tensor<1xf32> is the subtype.
    tensor_types.emplace_back(var_handle_op.resource_subtype());
  }

  auto builder =
      OpBuilder::atBlockTerminator(&session_init_func.getBody().front());