func.func @infer_var_handle_op_from_assigns() -> tensor<1xi8> {
    %cst = arith.constant dense<1> : tensor<1xi8>
    %0 = "tf.VarHandleOp"() {container = "", shared_name = "bar"} : () -> tensor<!tf_type.resource<tensor<*xi8>>>
    // CHECK: "tf.VarHandleOp"() <{container = "", shared_name = "bar"}> : () -> tensor<!tf_type.resource<tensor<1xi8>>>

  func.return %arg : tensor<!tf_type.resource<tensor<i32>>>
}

// CHECK-LABEL: func @side_effect_while_test
func.func @side_effect_while_test() -> (tensor<i32>) {
  %0 = "tf.VarHandleOp"() {device = "/device:CPU:0", container = "c", shared_name = "v"} : () -> tensor<!tf_type.resource<tensor<i32>>>
  // CHECK: [[while_res:%.]]:2 = tfrt.while {{%.*}} @"side_effect_while_body_add2/tfrt_body_1"

    %island = tf_executor.island {
    // expected-remark@above {{ID: 10}}
    // expected-remark@above {{Successors: {11}}}
      %vh0 = "tf.VarHandleOp"() {container = "c", shared_name = "v0"} : () -> tensor<*x!tf_type.resource<tensor<32xf32>>>
      // expected-remark@above {{ID: 0}}
      %vh1 = "tf.VarHandleOp"() {container = "c", shared_name = "v0"} : () -> tensor<*x!tf_type.resource<tensor<32xf32>>>
      // expected-remark@above {{ID: 1}}

    tf_device.return %1#0, %1#1 : tensor<!tf_type.string>, tensor<2x!tf_type.string>
  }) {device = "/device:CPU:0"} : () -> (tensor<!tf_type.string>, tensor<2x!tf_type.string>)
  %var = "tf.VarHandleOp"() {container = "c", shared_name = "v", device = "/device:TPU:0"} : () -> tensor<!tf_type.resource<tensor<3x3x1x32xf32>>>
  // CHECK-NOT: "tf.TPUGetLayoutOp"
  // CHECK-NOT: "tf.TPUCopyWithLayout"

  return VerifyUnsortedSegmentReduction(*this);
}

//===----------------------------------------------------------------------===//
// VarHandleOp
//===----------------------------------------------------------------------===//

LogicalResult VarHandleOp::verify() {
  // VarHandleOp requires the resource handle supply a single subtype from
  // which to derive the dtype and shape attributes.

  %outputs_8  =  "tf.Const"() {device = "/job:localhost/replica:0/task:0/device:CPU:0", value = dense<1> : tensor<i32>} : () -> tensor<i32>
  // CHECK: [[elems:%.*]] = "tf.VarHandleOp"
  %outputs_10  =  "tf.VarHandleOp"() {_xla_inferred_shapes = [#tf_type.shape<>], allowed_devices = [], container = "", device = "/job:localhost/replica:0/task:0/device:CPU:0", shared_name = "w"} : () -> tensor<!tf_type.resource<tensor<3x1xf32>>>

shared_name: the name by which this variable is referred to.
dtype and shape: attributes representing the data type and shape held in the
  variable.

Example:
    resource_variable_ops.var_handle_op(
          dtype=dtypes.int32, shape=[8, 16], container="foo", shared_name="bar")
  returns a handle for a variable with name "bar" in container "foo", and the
  variable holds a tensor of shape [8, 16] and dtype int32.
  }];

  func.return
}

// -----

// Valid VarHandleOp operation.
// CHECK-LABEL: func @testVarHandleOp
func.func @testVarHandleOp() -> tensor<!tf_type.resource<tensor<*xf32>>> {
  %0 = "tf.VarHandleOp"() {
    container = "",
    shared_name = "cd2c89b7-88b7-44c8-ad83-06c2a9158347"
  } : () -> tensor<!tf_type.resource<tensor<*xf32>>>

  func.return %0 : tensor<*xf32>
}

// CHECK-LABEL: @erase_tf_var_is_initialized
func.func @erase_tf_var_is_initialized(%arg0 : tensor<!tf_type.resource<tensor<f32>>>) -> tensor<i1> {
  %vh = "tf.VarHandleOp"() {container = "", shape = "tfshape$", shared_name = "x"} : () -> tensor<!tf_type.resource<tensor<f32>>>
  %is = "tf.VarIsInitializedOp"(%vh) : (tensor<!tf_type.resource<tensor<f32>>>) -> tensor<i1>

    if (std::optional<absl::string_view> cluster_name =
            GetXlaClusterForNode(*n)) {
      n->set_name(absl::StrCat(*cluster_name, "/", n->name()));
    } else if (n->type_string() == "VarHandleOp") {
      n->set_name(absl::StrCat("varhandle/", n->name()));
    } else {
      // There is room for improvement here.  In particular, it may help to