code: def takes(*argkeys):
      """Decorator which makes a DynamicItem and specifies its argkeys.
  
      If the wrapped object is a generator function (has a yield statement),
      Creates a GeneratorDynamicItem. If the object is already a DynamicItem,
      just specifies the argkeys for that. Otherwise creates a new regular
      DynamicItem, with argkeys specified.
  
      The args are always passed to the function at the start. Generators could
      support sending new arguments, but for such use cases, simply create a new
      dynamic item. The GeneratorDynamicItem class is meant for pipelines which
      take in an input and transform it in multiple ways, where the intermediate
      representations may be needed for e.g. fitting a BPE segmenter.
  
      Example
      -------
      >>> @takes("text")
      ... def tokenize(text):
      ...     return text.strip().lower().split()
      >>> tokenize.provides = ["tokenized"]
      >>> tokenize('\tThis Example gets tokenized')
      ['this', 'example', 'gets', 'tokenized']
      """
  
      def decorator(obj):
          """Decorator definition."""
          if isinstance(obj, DynamicItem):
              if obj.takes:
                  raise ValueError("Can't overwrite DynamicItem.takes")
              obj.takes = argkeys
              return obj
          elif inspect.isgeneratorfunction(obj):
              return GeneratorDynamicItem(takes=argkeys, func=obj)
          else:
              return DynamicItem(takes=argkeys, func=obj)
  
      return decorator
  
imports: import inspect
  from dataclasses import dataclass
  from speechbrain.utils.depgraph import DependencyGraph
label_desc: Use image node_id 1 for calling a global function with example usage: takes() and returns: decorator, obj, GeneratorDynamicItem, DynamicItem