func.func @init_func_restore_op() -> () attributes {tf_saved_model.exported_names = ["restore"], tf_saved_model.initializer_type = "restore_op"} {
        %cst = "tf.Const"() {device = "", value = dense<[1.0, 2.0]> : tensor<2xf32>} : () -> tensor<2xf32>
        %0 = "tf.VarHandleOp"() {container = "", device = "/device:CPU:0", shared_name = "var_0"} : () -> tensor<!tf_type.resource<tensor<2xf32>>>

indirect.module = "g:b"
indirect.version = "1.2"

incremental.group = "group"
incremental.name = "name"
incremental.version.require = "[1.0, 2.0["
incremental.version.prefer = "1.1"

incremental2.module = "group:name"
incremental2.version = { strictly = "[1.0, 2.0[", prefer = "1.5" }

groovy-with-ref = { group = "org.codehaus.groovy", name = "groovy", version.ref = "groovy" }

            dependencies {
                compile ':name1:1.0'
                compile ':name1:1.0'
                compile ':name2:[1.0, 2.0]'
                compile ':name3:1.0-SNAPSHOT'
                compile 'group1::1.0'
                compile 'group2::[1.0, 2.0]'
                compile 'group3::1.0-SNAPSHOT'
                compile 'group:name'
            }
        """
        failedResolve.prepare()

          public Integer load(Integer key) throws Exception {
            return i++;
          }
        };

    LoadingCache<Integer, Integer> cache = CacheBuilder.newBuilder().build(loader);

    cache.put(10, 20);

    Map<Integer, Integer> map = cache.getAll(ImmutableList.of(10, 20, 30, 54, 443, 1));

    assertEquals(Integer.valueOf(20), map.get(10));
    assertEquals(Integer.valueOf(0), map.get(20));

          public Integer load(Integer key) throws Exception {
            return i++;
          }
        };

    LoadingCache<Integer, Integer> cache = CacheBuilder.newBuilder().build(loader);

    cache.put(10, 20);

    Map<Integer, Integer> map = cache.getAll(ImmutableList.of(10, 20, 30, 54, 443, 1));

    assertEquals(Integer.valueOf(20), map.get(10));
    assertEquals(Integer.valueOf(0), map.get(20));

indirect.module = "g:b"
indirect.version = "1.2"

incremental.group = "group"
incremental.name = "name"
incremental.version.prefer = "1.1"
incremental.version.require = "[1.0, 2.0["

incremental2.module = "group:name"
incremental2.version = { strictly = "[1.0, 2.0[", prefer = "1.5" }

groovy-with-ref = { group = "org.codehaus.groovy", name = "groovy", version.ref = "groovy" }

package main

import "runtime"

func main() {
	{
		x := inuse()
		c := make(chan []byte, 10)
		c <- make([]byte, 10<<20)
		close(c)
		f1(c, x)
	}
	{
		x := inuse()
		c := make(chan []byte, 10)
		c <- make([]byte, 10<<20)
		close(c)
		f2(c, x)
	}
}

func f1(c chan []byte, start int64) {
	for x := range c {
		if delta := inuse() - start; delta < 9<<20 {

//line :1
	check("??", 1) // no file specified
//line foo.go:1
	check("foo.go", 1)
//line bar.go:10:20
	check("bar.go", 10)
//line :11:22
	check("bar.go", 11) // no file, but column specified => keep old filename

/*-style line directives */
/*line :1*/ check("??", 1) // no file specified
/*line foo.go:1*/ check("foo.go", 1)
/*line bar.go:10:20*/ check("bar.go", 10)

}

// CHECK-LABEL: Batchmatmul2Fullyconnected
// CHECK-NOT: "tfl.batch_matmul"
func.func @Batchmatmul2Fullyconnected(%arg0: tensor<4x128x2xf32>) -> (tensor<4x128x1xf32>) {
  %0 = arith.constant dense<[[1.0], [2.0]]> : tensor<2x1xf32>
  %1 = "tfl.batch_matmul"(%arg0, %0) {adj_x = false, adj_y = false, asymmetric_quantize_inputs = false} : (tensor<4x128x2xf32>, tensor<2x1xf32>) -> tensor<4x128x1xf32>
  func.return %1 : tensor<4x128x1xf32>

	if m.Version == 0 {
		b[2] = syscall.RTM_VERSION
	} else {
		b[2] = byte(m.Version)
	}
	b[3] = byte(m.Type)
	nativeEndian.PutUint16(b[4:6], uint16(sizeofRtMsghdr))
	nativeEndian.PutUint32(b[16:20], uint32(m.Flags))
	nativeEndian.PutUint16(b[6:8], uint16(m.Index))
	nativeEndian.PutUint32(b[24:28], uint32(m.ID))
	nativeEndian.PutUint32(b[28:32], uint32(m.Seq))
	attrs, err := marshalAddrs(b[sizeofRtMsghdr:], m.Addrs)