original_docstring: Predict outcome for each input data in `x_test`.
  
          Arguments:
              x_test: Input data whose shape is (n_samples, n_features).
  
          Returns:
              y_pred: Predicted outcome whose shape is (n_samples,).
          
docstring: Predict outcome for each input data in `x_test`.
docstring_tokens: Predict outcome for each input data in ` x_test ` .
code: def predict(self, x_test: NDArray[np.float_]) -> NDArray[np.int_]:
          """Predict outcome for each input data in `x_test`.
  
          Arguments:
              x_test: Input data whose shape is (n_samples, n_features).
  
          Returns:
              y_pred: Predicted outcome whose shape is (n_samples,).
          """
          if x_test.ndim == 1:
              x_test = x_test.reshape((-1, 1))
          if self.do_x_scale:
              x_scaled = self.scale_x_scaler.transform(x_test)
          else:
              x_scaled = x_test
  
          y_pred: NDArray[np.int_] = self._predict_inner(x_scaled).argmax(axis=1)
          return y_pred
code_tokens: def predict ( self , x_test : NDArray [ np . float_ ] ) -> NDArray [ np . int_ ] : if x_test . ndim == 1 : x_test = x_test . reshape ( ( - 1 , 1 ) ) if self . do_x_scale : x_scaled = self . scale_x_scaler . transform ( x_test ) else : x_scaled = x_test y_pred : NDArray [ np . int_ ] = self . _predict_inner ( x_scaled ) . argmax ( axis = 1 ) return y_pred
short_docstring: Predict outcome for each input data in `x_test`.
short_docstring_tokens: Predict outcome for each input data in ` x_test ` .
comment: """Predict outcome for each input data in `x_test`.
  
          Arguments:
              x_test: Input data whose shape is (n_samples, n_features).
  
          Returns:
              y_pred: Predicted outcome whose shape is (n_samples,).
          """