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);
  }

    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

  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 %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`.