if (auto pop_back = dyn_cast<TF::TensorListPopBackOp>(op)) {
    return InferShapeForTensorListPopBackOp(pop_back);
  }

  if (auto var_handle_op = dyn_cast<VarHandleOp>(op)) {
    return InferShapeForVarHandleOp(var_handle_op);
  }

  if (auto xla_reduce_window_op = dyn_cast<XlaReduceWindowOp>(op)) {
    return InferShapeForXlaReduceWindowOp(xla_reduce_window_op);
  }

  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>>>

    to input arguments and outputs of the function(s).

    Two types of resources are supported:
    (1) A function argument of TF::ResourceType type (this pass).
    (2) A VarHandleOp in the function (tf-promote-var-handles-to-args).

    After the pass,

     . The function will have an input argument for each resource that is

    %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}}

  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.

  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>

  Args:
    node_def: `NodeDef` to test whether it is a variable or not.

  Returns:
    Returns True if it is a variable.
  """
  return node_def.op == 'VarHandleOp'


def _find_variables(
    graph_def: graph_pb2.GraphDef,
) -> Mapping[str, node_def_pb2.NodeDef]:
  """Finds all variables within `graph_def`.