%default = "tf.Const"() {value = dense<-1> : tensor<i64>} : () -> tensor<i64>
  %handle = "tf.HashTableV2"() {container = "", device = "/job:localhost/replica:0/task:0/device:CPU:0", key_dtype = !tf_type.string, shared_name = "hash_table", use_node_name_sharing = false, value_dtype = i64} : () -> tensor<!tf_type.resource>

  // CHECK: tf.LookupTableFindV2
  // CHECK-NOT: tf.Assert

# CHECK-SAME: tf_saved_model.initializer_type = "init_op"
# CHECK-NEXT: [[R6:%.*]] = "tf.Const"()
# CHECK-NEXT: [[R5:%.*]] = "tf.Const"() <{value = dense<[1, 2,
# CHECK-NEXT: [[R7:%.*]] = "tf.HashTableV2"()
# CHECK-SAME: shared_name = "[[hash_table:.*]]"
# CHECK-NEXT: "tf.LookupTableImportV2"([[R7]], [[R5]], [[R6]])

# CHECK:      func {{@[a-zA-Z_0-9]+}}(
# CHECK-SAME: [[ARG0:%.*]]: tensor<i32>

  // CHECK-SAME: tf_saved_model.initializer_type = "restore_op"
  // CHECK: %[[CST:.*]] = "tf.Const"()
  // CHECK-NEXT: %[[HASH_TABLE:.*]] = "tf.HashTableV2"()
  // CHECK-NEXT: "tf.AssignVariableOp"(%[[HASH_TABLE]], %[[CST]])
}

// -----


// Nothing happens when there are no `tf_saved_model.session_initializer`.

// CHECK-LABEL: module
module attributes {tf_saved_model.semantics} {
}

// -----

}

// CHECK-LABEL: func @set_readonly_stateless
func.func @set_readonly_stateless(%arg: tensor<i32>, %cond: tensor<i1>) -> tensor<i32> {
  %handle = "tf.HashTableV2"() {container = "", key_dtype = i32, shared_name = "hash_table", use_node_name_sharing = false, value_dtype = i32} : () -> tensor<!tf_type.resource>
  // CHECK: tf.While
  // CHECK-SAME: is_stateless = true

# CHECK-SAME: [[ARG1:%.*]]: tensor<!tf_type.string> {tf_saved_model.bound_input = @[[asset1]]}
# CHECK-SAME: tf_saved_model.initializer_type = "init_op"
# CHECK-NEXT: [[R0:%.*]] = "tf.HashTableV2"()
# CHECK-SAME: shared_name = "[[hash_table:.*]]"
# CHECK-NEXT: "tf.InitializeTableFromTextFileV2"([[R0]], [[ARG0]])


def write_vocabulary_file(vocabulary):
  """Write temporary vocab file for module construction."""

		return 0
	}
	d.linkctxt.Errorf(symIdx, "internal error: no entry for sym %d in rtmap\n", symIdx)
	return 0
}

// Find child by AT_name using hashtable if available or linear scan
// if not.
func findchild(die *dwarf.DWDie, name string) *dwarf.DWDie {
	var prev *dwarf.DWDie
	for ; die != prev; prev, die = die, walktypedef(die) {

// high-order bits of each hash to distinguish the entries
// within a single bucket.
//
// If more than 8 keys hash to a bucket, we chain on
// extra buckets.
//
// When the hashtable grows, we allocate a new array
// of buckets twice as big. Buckets are incrementally
// copied from the old bucket array to the new bucket array.
//
// Map iterators walk through the array of buckets and

 */

package org.gradle.internal.snapshot.impl;

import com.google.common.collect.ImmutableList;
import org.gradle.internal.hash.Hashable;
import org.gradle.internal.hash.Hasher;

class AbstractMapSnapshot<T extends Hashable> implements Hashable {
    protected final ImmutableList<MapEntrySnapshot<T>> entries;

    public AbstractMapSnapshot(ImmutableList<MapEntrySnapshot<T>> entries) {

// CHECK-SAME: {tf_saved_model.exported_names = ["__tf_saved_model_session_initializer_NoOp"]}
// CHECK: %[[HASH_TABLE0:.*]] = "tf.HashTableV2"()
// CHECK: "tf.InitializeTableFromTextFileV2"(%[[HASH_TABLE0]], %[[ARG0]])
// CHECK: return

 * limitations under the License.
 */

package org.gradle.internal.snapshot.impl;

import org.gradle.internal.hash.Hashable;
import org.gradle.internal.hash.Hasher;

class AbstractManagedValueSnapshot<T extends Hashable> implements Hashable {
    protected final T state;

    public AbstractManagedValueSnapshot(T state) {
        this.state = state;
    }

    public T getState() {