// Create 2 resources and initialize them with dummy values.
  TF_CHECK_OK(device->resource_manager()->Create(
      container, "var1", new tensorflow::Var(tensorflow::DataType::DT_FLOAT)));
  TF_CHECK_OK(device->resource_manager()->Create(
      container, "var2", new tensorflow::Var(tensorflow::DataType::DT_FLOAT)));
}

Status FakeSession::Create(const tensorflow::GraphDef& graph) {

	for tok := s.Scan(); tok != scanner.EOF; tok = s.Scan() {
		fmt.Printf("%s: %s\n", s.Position, s.TokenText())
	}

	// Output:
	// default:1:1: %
	// default:1:2: var1
	// default:1:7: var2
	// default:1:11: %
	//
	// percent:1:1: %var1
	// percent:1:7: var2
	// percent:1:11: %
}

func Example_mode() {
	const src = `
    // Comment begins at column 5.

This line should not be included in the output.

/*

        if (!(o instanceof TypeVariableTypeWrapper)) {
            return false;
        } else {
            TypeVariableTypeWrapper var2 = (TypeVariableTypeWrapper) o;
            return Objects.equal(this.getName(), var2.getName())
                && Arrays.equals(this.bounds, var2.bounds);
        }
    }

    @Override
    public int hashCode() {
        return hashCode;
    }

    func.return %4 : tensor<100x50xf32>
  }
  // CHECK: "tf.VarHandleOp"
  // CHECK-SAME: shared_name = "var1"
  // CHECK-SAME: _is_initialized = true
  // CHECK: "tf.VarHandleOp"
  // CHECK-SAME: shared_name = "var2"
  // CHECK-SAME: _is_initialized = true
  // CHECK: "tf.VarHandleOp"

        if (!(o instanceof WildcardTypeWrapper)) {
            return false;
        } else {
            WildcardTypeWrapper var2 = (WildcardTypeWrapper) o;
            return Arrays.equals(this.lowerBounds, var2.lowerBounds) && Arrays.equals(this.upperBounds, var2.upperBounds);
        }
    }

    @Override
    public int hashCode() {
        return hashCode;
    }

    @Override

    // CHECK: %[[VAR0:.*]] = "tf.VarHandleOp"()
    %0 = "tf.VarHandleOp"() {container = "", shared_name = "var0"} : () -> tensor<!tf_type.resource<tensor<!tf_type.string>>>

    // CHECK: "tf.AssignVariableOp"(%[[VAR0]], %[[ASSET0]])
    "tf.AssignVariableOp"(%0, %arg0) : (tensor<!tf_type.resource<tensor<!tf_type.string>>>, tensor<!tf_type.string>) -> ()

    // CHECK: %[[VAR1:.*]] = "tf.VarHandleOp"()

  // CHECK-SAME: tf_saved_model.initializer_type = "restore_op"
  // CHECK: %[[VAR:.*]] = "tf.VarHandleOp"()
  // CHECK-SAME: "var1"
  // CHECK: %[[CST:.*]] = arith.constant dense<> : tensor<0xf32>
  // CHECK: "tf.AssignVariableOp"(%[[VAR]], %[[CST]])
}


// -----

// Test invalid tensor
// expected-error@below{{Can't find variable var5 in session}}
module attributes {tf_saved_model.semantics, tf_saved_model.under_construction} {

from tensorflow.compiler.mlir.tensorflow.tests.tf_saved_model import common_v1

# CHECK: "tf_saved_model.global_tensor"() <{is_mutable, sym_name = "[[VAR0:[a-zA-Z_0-9]+]]", type = tensor<5x3xf32>, value = {{.*}} : tensor<5x3xf32>}> : () -> ()
# CHECK: "tf_saved_model.global_tensor"() <{is_mutable, sym_name = "[[VAR1:[a-zA-Z_0-9]+]]", type = tensor<3x5xf32>, value = {{.*}} : tensor<3x5xf32>}> : () -> ()
# CHECK:      func {{@[a-zA-Z_0-9]+}}(

		{JSONSchemaPropsOrBoolHolder{JSPoBOmitEmpty: &JSONSchemaPropsOrBool{Schema: &JSONSchemaProps{Type: "string"}}}, `{"val1":false,"val2":{"type":"string"}}`},
		{JSONSchemaPropsOrBoolHolder{JSPoBOmitEmpty: &JSONSchemaPropsOrBool{}}, `{"val1":false,"val2":false}`},
		{JSONSchemaPropsOrBoolHolder{JSPoBOmitEmpty: &JSONSchemaPropsOrBool{Allows: true}}, `{"val1":false,"val2":true}`},
	}

VariableInfo::VariableInfo(
    int index, absl::string_view name, Var* var,
    const std::optional<ManagedStackTrace>& definition_stack_trace)
    : index_(index),
      name_(name),
      var_(var),
      definition_stack_trace_(definition_stack_trace) {}

VariableInfo::VariableInfo(VariableInfo&& other)
    : index_(other.index_),
      var_(other.var_),
      definition_stack_trace_(other.definition_stack_trace_),