code: def fit(self):
      lTrendEstimFrame = self.mSplit.getEstimPart(self.mTrendFrame)
      est_target = lTrendEstimFrame[self.mSignal].values
      est_inputs = lTrendEstimFrame[
          [self.mTimeInfo.mNormalizedTimeColumn]
      ].values
      self.mTrendRidge.fit(est_inputs, est_target)
      self.mTrendRidge.score(est_inputs, est_target)
      target = self.mTrendFrame[self.mSignal].values
      inputs = self.mTrendFrame[
          [self.mTimeInfo.mNormalizedTimeColumn]
      ].values
      self.mTrendFrame[self.mOutName] = self.mTrendRidge.predict(inputs)
      self.mTrendFrame[self.mOutName + "_residue"] = (
          target - self.mTrendFrame[self.mOutName].values
      )
  
imports: import pandas
  import numpy
  from .None import Time
  from .None import Perf
  from .None import Plots
  from .None import Utils
  import sklearn
  import sklearn.preprocessing
  import sklearn.linear_model
  from sklearn.feature_selection import RFE
  from sklearn.linear_model import LinearRegression
label_desc: Use image node_id 3 for calling the cLinearTrend obj's underlying member method code with example usage: obj.fit() without return types