code: def fit(self, X, Y, sample_weight):
      sqrtW = np.sqrt(np.array(sample_weight))
      matX = np.array(X) * sqrtW[:, np.newaxis]
      vecY = Y * sqrtW
      self.lsqinfo = np.linalg.lstsq(matX, vecY, rcond=-1)
      self.weights = pd.Series(self.lsqinfo[0], index=list(X))
  
file_path: fairlearn/test/unit/reductions/exponentiated_gradient/simple_learners.py
imports: import numpy
  import pandas
label_desc: Use image node_id 2 for calling the LeastSquaresBinaryClassifierLearner obj's underlying member method code with example usage: obj.fit(X, Y, sample_weight) without return types