code: def predict(self, X: np.ndarray, **kwargs):
      forecast = getattr(self.model, self.forecast_function)(
          X, **kwargs
      )  # of length forecast horizon
      forecast = np.asarray(forecast)
      if (forecast.shape[0] > 1) and (
          forecast.shape[0] == X.shape[0]
      ):  # batch forecast
          return forecast
      else:
          forecast = forecast.reshape(-1, 1)
          return np.mean(forecast, axis=0)
  
imports: from typing import Callable
  import numpy
label_desc: Use image node_id 2 for calling the Forecaster obj's underlying member method code with example usage: obj.predict(X) and returns: forecast, np