class_docstr: A step in the pipeline.
  
  Parameters
  ----------
  skobject : BaseObject
      The sktime object that is used in the step.
  name : str
      The name of the step.
  input_edges : dict
      A dict with string keys to string values. Identifying the
      predcessors.  The keys of the edges dict specify to which argument
      of fit/predict/.. the output of the predecessors (the value of the
      dict specifies the predecessors name) should be passed.
  method : str
      The method that should be called on the skobject. If None, the pipeline
       selects the method based on the method
       that is called on the pipeline (e.g., predict, transform, ..)
  params : dict
      The parameters that should be passed to the skobject when calling
      method
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code: def _extract_kwargs(self, method_name, kwargs):
      kwargs_ = deepcopy(kwargs)
      kwargs_.update(self.params)
      use_kwargs = {}
      method = getattr(self.skobject, method_name)
      method_signature = inspect.signature(method).parameters
  
      for name, _param in method_signature.items():
          if name in kwargs:
              use_kwargs[name] = kwargs[name]
      return use_kwargs
  
file_path: sktime/sktime/pipeline/step.py
imports: import inspect
  from copy import deepcopy
  import pandas
  from sktime.forecasting.base import BaseForecaster
label_desc: Use image node_id 6 for calling the Step obj's underlying member method code with example usage: obj._extract_kwargs(method_name, kwargs) and returns: use_kwargs