class_docstr: A wrapper that allows us to set parameters in the constructor and do a reset before fitting.
      
class_docstr_tok: ["A","wrapper","that","allows","us","to","set","parameters","in","the","constructor","and","do","a","reset","before","fitting","."]
code: def predict(self, X):
      X = self.scaler.transform(X)
      if self.flatten_output:
          return self.model.predict(X).flatten()
      else:
          return self.model.predict(X)
  
imports: import numpy
  import sklearn
  import sklearn.ensemble
  from sklearn.preprocessing import StandardScaler
label_desc: Use image node_id 3 for calling the KerasWrap obj's underlying member method code with example usage: obj.predict(X) and returns: self, self