# Text form of tensorflow.tf2xla.Config proto.
fetch {
  id { node_name: "result" }
}

variable {
  node_name: "x"
  shape {
    dim { size: 1 }
  }
  type: DT_FLOAT

    "func.return"() : () -> ()
  }) {sym_name = "__inference_wrapped_function_4260250_0", sym_visibility = "private", tf._input_shapes = [#tf_type.shape<>], tf.signature.is_stateful, function_type = (tensor<i32>) -> ()} : () -> ()
  "func.func"() ({
  ^bb0(%arg0: tensor<i32>):
    "func.return"(%arg0) : (tensor<i32>) -> ()

    func.return %4 : tensor<100x50xf32>
  }
  // CHECK: "tf_saved_model.global_tensor"()
  // CHECK:    sym_name = "dense/kernel"
  // CHECK: "tf_saved_model.global_tensor"()
  // CHECK:    sym_name = "dense/bias"
  // CHECK:  func @serving_default(
  // CHECK:    %arg0: tensor<!tf_type.resource<tensor<100x50xf32>>> {tf_saved_model.bound_input = @"dense/kernel"},

      Roboto
    {%- endif -%}

  # Site name
  - &site_name >-
    {{ config.site_name }}

  # Page title
  - &page_title >-
    {{ page.meta.get("title", page.title) }}

  # Page title with site name
  - &page_title_with_site_name >-
    {%- if not page.is_homepage -%}
      {{ page.meta.get("title", page.title) }} - {{ config.site_name }}
    {%- else -%}
      {{ page.meta.get("title", page.title) }}

  // CHECK: ml_program.global private mutable [[V:@.+]](dense<1.000000e+00> : tensor<1xf32>) : tensor<?xf32>
  // CHECK: func.func @f
  // CHECK: [[T:%.+]] = ml_program.global_load [[V]]
  "tf_saved_model.global_tensor"() { is_mutable, sym_name = "v", type = tensor<?xf32>, value = dense<1.> : tensor<1xf32> } : () -> ()
  func.func @f(%arg0: tensor<!tf_type.resource<tensor<?xf32>>> {tf_saved_model.bound_input = @v})
  -> (tensor<?xf32> {tf_saved_model.index_path = []})

          custom_op_map[node_name].quantizable_input_indices.push_back(
              std::stoi(cur_index));
        }
        break;
      }
      case CustomOpUpdateOptions::kWeightOnly:
        custom_op_map[node_name].is_weight_only =
            GetBooleanSpecs(node_specification);
        break;
      case CustomOpUpdateOptions::kNoSideEffect:
        custom_op_map[node_name].no_side_effect =

// CHECK-LABEL: module
module attributes {tf_saved_model.semantics} {
  // CHECK: ml_program.global {{.*}} @vars.v
  // CHECK: ml_program.global {{.*}} @vars.bar
  "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> } : () -> ()
  func.func @read(%arg0: tensor<!tf_type.resource<tensor<10xf32>>> {tf_saved_model.bound_input = @v})

constexpr std::string_view kQuantizationUnitSuffix = ")";

// Concatenates node name and func name with a "@" separator.
std::string ConcatNodeAndFuncName(std::string_view node_name,
                                  std::string_view func_name) {
  return absl::StrCat(node_name, "@", func_name);
}

// Generate a string to represent the QuantizationUnit.
std::string GenerateQuantizationUnitString(

  // Check for a few expected nodes
  const char* node_name = "test_loop/cond/scalar";
  EXPECT_TRUE(TF_GraphOperationByName(graph_, node_name) != nullptr);
  node_name = "test_loop/body/add";
  EXPECT_TRUE(TF_GraphOperationByName(graph_, node_name) != nullptr);
  node_name = "test_loop/body/inner_loop/body/one";
  EXPECT_TRUE(TF_GraphOperationByName(graph_, node_name) != nullptr);
  node_name = "test_loop/body/inner_loop/cond/less_than";

# CHECK-SAME: initializers = [@[[restore:.*]]]

# CHECK: "tf_saved_model.asset"()
# CHECK-SAME: <{filename = [[filename:.*]], sym_name = "[[sym_name:.*]]"}> : () -> ()

# CHECK:      func @[[restore]](
# CHECK-SAME:   [[variable_path:%.*]]: tensor<!tf_type.string> {tf_saved_model.bound_input = @[[sym_name]]}
# CHECK-SAME: tf_saved_model.exported_names = ["{{__tf_saved_model_session_initializer.*}}"]