"tf_saved_model.global_tensor"() { is_mutable, sym_name = "v0", type = tensor<3xf32>, value = dense<42.0> : tensor<9xf32> } : () -> ()
}

// -----

module attributes {tf_saved_model.semantics} {
  "tf_saved_model.global_tensor"() { is_mutable, sym_name = "v", type = tensor<f32>, value = dense<42.0> : tensor<f32> } : () -> ()

            </arguments>
        </buildCommand>
    </buildSpec>
    <natures>
        <nature>org.eclipse.jdt.core.scalanature</nature>
    </natures>
    <linkedResources>
        <link>
            <name>somename</name>
            <type>sometype</type>
            <location>somelocation</location>
        </link>
    </linkedResources>
    <filteredResources>
        <filter>
            <id>1</id>

// RUN: tf-opt %s -allow-unregistered-dialect --tf-localize-var-handles --split-input-file | FileCheck %s

// CHECK-LABEL: module
module attributes {tf_saved_model.semantics} {
  "tf_saved_model.global_tensor"() { is_mutable, sym_name = "v", type = tensor<10xf32>, value = dense<[0.,1.,2.,3.,4.,5.,6.,7.,8.,9.]> : tensor<10xf32> } : () -> ()
  // CHECK-LABEL: @read_from_global

	if err != nil {
		t.Fatalf("unexpected error marshalling: %v", err)
	}

	pod2, ok := obj2.(*corev1.Pod)
	if !ok {
		t.Fatal("did not get a Pod")
	}

	if pod2.Name != "someName" {
		t.Errorf("expected someName, got %q", pod2.Name)
	}

	if pod2.Namespace != "testNamespace" {
		t.Errorf("expected testNamespace, got %q", pod2.Namespace)
	}

  // CHECK: "tf_saved_model.global_tensor"() <{
  // CHECK-NOT: is_mutable
  "tf_saved_model.global_tensor"() { is_mutable, sym_name = "v", type = tensor<f32>, value = dense<42.> : tensor<f32> } : () -> ()

  // CHECK: "tf_saved_model.global_tensor"() <{
  // CHECK-NOT: is_mutable
  "tf_saved_model.global_tensor"() { is_mutable, sym_name = "v2", type = tensor<f32>, value = dense<42.> : tensor<f32> } : () -> ()

# CHECK: "tf_saved_model.session_initializer"() <{initializers = [@[[init:.*]]]}> : () -> ()
# CHECK: "tf_saved_model.asset"() <{filename = {{.*}}, sym_name = "[[asset1:__tf_saved_model_asset1_.*]]"}>
# CHECK: "tf_saved_model.asset"() <{filename = {{.*}}, sym_name = "[[asset0:__tf_saved_model_asset0_.*]]"}>

# CHECK:      func @[[init]]
# CHECK-SAME: [[ARG0:%.*]]: tensor<!tf_type.string> {tf_saved_model.bound_input = @[[asset0]]}

module attributes {tf.versions = {bad_consumers = [], min_consumer = 0 : i32, producer = 398 : i32}, tf_saved_model.semantics} {
  "tf_saved_model.global_tensor"() {is_mutable, sym_name = "y", type = tensor<3x1xi32>, value = dense<[[1], [2], [3]]> : tensor<3x1xi32>} : () -> ()
  "tf_saved_model.asset"() {sym_name = "z", filename = "file"} : () -> ()
  func.func @serving_default(
      %arg0: tensor<1x3xi32> {tf_saved_model.index_path = ["x"]},

  // Test case: Remove duplicate bound_input symbols.
  "tf_saved_model.global_tensor"() { is_mutable, sym_name = "v", type = tensor<f32>, value = dense<42.0> : tensor<f32> } : () -> ()
  "tf_saved_model.global_tensor"() { is_mutable, sym_name = "w", type = tensor<f32>, value = dense<43.0> : tensor<f32> } : () -> ()
  "tf_saved_model.global_tensor"() { is_mutable, sym_name = "x", type = tensor<f32>, value = dense<44.0> : tensor<f32> } : () -> ()
  // CHECK: func @f

    func.return
  }
}

// -----

module attributes {tf_saved_model.semantics} {

  "tf_saved_model.asset"() {filename = "assets/table.txt", sym_name = "v"} : () -> ()
  "tf_saved_model.asset"() {filename = "assets/table2.txt", sym_name = "w"} : () -> ()

  // CHECK: func @f()
  func.func @f(%arg0: tensor<!tf_type.string> {tf_saved_model.bound_input = @v}, %arg1: tensor<!tf_type.string> {tf_saved_model.bound_input = @w})

  // Builds TF operation and sets all the attributes.
  std::string node_name = "unnamed";
  if (auto attr = inst->getAttrOfType<mlir::StringAttr>("name")) {
    node_name = std::string(attr.getValue());
  }
  auto node_def_or = ConvertTFDialectOpToNodeDef(
      inst, node_name.c_str(), /*ignore_unregistered_attrs=*/true);
  RETURN_FAILURE_IF_ERROR(node_def_or.status());