code: def make_pipeline(*steps):
      """Create a pipeline from estimators of any type.
  
      Parameters
      ----------
      steps : tuple of sktime estimators
          in same order as used for pipeline construction
  
      Returns
      -------
      pipe : sktime pipeline containing steps, in order
          always a descendant of BaseObject, precise object determined by scitype
          equivalent to result of step[0] * step[1] * ... * step[-1]
  
      Examples
      --------
      Example 1: forecaster pipeline
  
      >>> from sktime.pipeline import make_pipeline
      >>> from sktime.transformations.series.exponent import ExponentTransformer
      >>> from sktime.forecasting.trend import PolynomialTrendForecaster
      >>> pipe = make_pipeline(ExponentTransformer(), PolynomialTrendForecaster())
      >>> type(pipe).__name__
      'TransformedTargetForecaster'
  
      Example 2: classifier pipeline
  
      >>> from sktime.pipeline import make_pipeline
      >>> from sktime.transformations.series.exponent import ExponentTransformer
      >>> from sktime.classification.distance_based import KNeighborsTimeSeriesClassifier
      >>> pipe = make_pipeline(ExponentTransformer(), KNeighborsTimeSeriesClassifier())
      >>> type(pipe).__name__
      'ClassifierPipeline'
  
      Example 3: transformer pipeline
  
      >>> from sktime.pipeline import make_pipeline
      >>> from sktime.transformations.series.exponent import ExponentTransformer
      >>> pipe = make_pipeline(ExponentTransformer(), ExponentTransformer())
      >>> type(pipe).__name__
      'TransformerPipeline'
      """
      pipe = steps[0]
      for i in range(1, len(steps)):
          pipe = pipe * steps[i]
  
      return pipe
  
label_desc: Use image node_id 1 for calling a global function with example usage: make_pipeline() and returns: pipe