code: def create_polynom_regressor(
      degree: int = 2, **kwargs_linear_model
  ) -> SklearnRegressionModel:
      return SklearnRegressionModel(
          make_pipeline(
              PolynomialFeatures(degree=degree, include_bias=False),
              LinearRegression(**kwargs_linear_model),
          )
      )
  
imports: from abc import abstractmethod
  from typing import Any
  import numpy
  import sklearn
  from packaging import version
  from sklearn.pipeline import make_pipeline
  from sklearn.preprocessing import PolynomialFeatures
  from sklearn.ensemble import AdaBoostRegressor, ExtraTreesRegressor, HistGradientBoostingRegressor, RandomForestRegressor
  from sklearn.gaussian_process import GaussianProcessRegressor
  from sklearn.linear_model import ElasticNetCV, LassoCV, LassoLarsIC, LinearRegression, RidgeCV
  from sklearn.neighbors import KNeighborsRegressor
  from sklearn.svm import SVR
  from dowhy.gcm.ml.prediction_model import PredictionModel
  from dowhy.gcm.util.general import auto_apply_encoders, auto_fit_encoders, shape_into_2d
label_desc: Use image node_id 14 for calling a global function with example usage: create_polynom_regressor(degree) and returns: SklearnRegressionModel