human
Solve the following python programming problem given the following code:
@ skip_if_cached 
def run_ParDo(self, transform_node) : 
    transform = transform_node.transform 
    context = DoFnProcessContext (label = transform.label, state = DoFnState (self._counter_factory)) 
    side_inputs = [self._cache.get_pvalue (view) for view in transform_node.side_inputs] 
    options = transform_node.inputs [0].pipeline.options 
    if ((options is not None) and options.view_as (TypeOptions).runtime_type_check) : 
        transform.dofn = TypeCheckWrapperDoFn (value.dofn, transform.get_type_hints ()) 
transform.dofn = OutputCheckWrapperDoFn (transform.dofn, transform_node.full_label) 
    class RecordingReceiverSet (object) : 
        def __init__(self, tag) : 
            self.tag = tag 
def output(self, element) : 
            results [self.tag].append (element) 
class TaggedReceivers (dict) : 
        def __missing__(self, key) : 
            return RecordingReceiverSet (key) 
results = collections.defaultdict (list) 
    for tag in transform.side_output_tags : 
        results [tag] = [] 
runner = DoFnRunner (transform.dofn, transform.args, transform.kwargs, side_inputs, transform_node.inputs [0].windowing, context, TaggedReceivers (), step_name = transform_node.full_label) 
    runner.start () 
    for v in self._cache.get_pvalue (transform_node.inputs [0]) : 
        runner.process (v) 
runner.finish () 
    self._cache.cache_output (transform_node, []) 
    for (tag, value) in results.items () : 
        self.debug_counters ['element_counts'] [(transform_node.full_label, tag)] += len (value) 
        self._cache.cache_output (transform_node, tag, value) 

    
    

    Fix the buggy line: transform.dofn = TypeCheckWrapperDoFn (value.dofn, transform.get_type_hints ())
assistant
#Here is Python program to fix the buggy line: transform.

transform.dofn = TypeCheckWrapperDoFn (transform.dofn, transform.get_type_hints ())