code: def _wrap_iterator(op_class, op_name, wrapper_name):
      """Wraps reader Eager Operator in a Python iterator.
  
      Example:
          >>> for file, label in eager.readers.file(file_root=file_path, batch_size=8):
          ...     # file and label are batches of size 8 (TensorLists).
          ...     print(file)
      """
  
      def wrapper(*inputs, **kwargs):
          if len(inputs) > 0:
              raise ValueError(
                  "Iterator type eager operators should not receive any inputs."
              )
  
          inputs, init_args, call_args = _prep_args(
              inputs,
              kwargs,
              op_name,
              wrapper_name,
              _iterator_op_factory.disqualified_arguments,
          )
  
          op = _iterator_op_factory(
              op_class, wrapper_name, len(inputs), call_args.keys()
          )(call_args, **init_args)
  
          return op
  
      return wrapper
  
imports: import sys
  from nvidia.dali import backend
  from nvidia.dali import internal
  from nvidia.dali import ops
  from nvidia.dali import tensors
  from nvidia.dali import types
  from nvidia.dali.external_source import _prep_data_for_feed_input
label_desc: Use image node_id 45 for calling a global function with example usage: _wrap_iterator(op_class, op_name, wrapper_name) and returns: wrapper, op