code: def fitOne(model, X, y, params):
      """
      Makes one model fit using provided data and parameters
      :param model: The instantiated model you wish to pass, e.g. LogisticRegression()
      :param X: The independent variable data
      :param y: The response variable data
      :param params: The parameters passed through to the model from the parameter grid
      :return: Returns the fitted model
      """
      if isinstance(model, BaseEstimator):
          model.set_params(**params)
      else:
          model = model(**params)
      return model.fit(X, y)
  
imports: from joblib import Parallel, delayed
  from sklearn.base import BaseEstimator
  import warnings
label_desc: Use image node_id 1 for calling a global function with example usage: fitOne(model, X, y, params) and returns: model