class_docstr: A `Predictor` that uses the last `prediction_length` observations to
  predict the future.
  
  Parameters
  ----------
  prediction_length
      Prediction horizon.
  num_samples
      Number of samples to include in the forecasts. Not that the samples
      produced by this predictor will all be identical.
class_docstr_tok: ["A","`","Predictor","`","that","uses","the","last","`","prediction_length","`","observations","to","predict","the","future",".","Parameters","--","--","--","--","--","prediction_length","Prediction","horizon",".","num_samples","Number","of","samples","to","include","in","the","forecasts",".","Not","that","the","samples","produced","by","this","predictor","will","all","be","identical","."]
code: def predict_item(self, item: DataEntry) -> Forecast:
      prediction = item["target"][-self.prediction_length :]
      samples = np.broadcast_to(
          array=np.expand_dims(prediction, 0),
          shape=(self.num_samples, self.prediction_length),
      )
  
      return SampleForecast(
          samples=samples,
          start_date=forecast_start(item),
          item_id=item.get(FieldName.ITEM_ID),
      )
  
file_path: gluonts/src/gluonts/model/trivial/identity.py
imports: import numpy
  from gluonts.core.component import validated
  from gluonts.dataset.common import DataEntry
  from gluonts.dataset.field_names import FieldName
  from gluonts.dataset.util import forecast_start
  from gluonts.model.forecast import Forecast, SampleForecast
  from gluonts.model.predictor import RepresentablePredictor
label_desc: Use image node_id 2 for calling the IdentityPredictor obj's underlying member method code with example usage: obj.predict_item(item) and returns: SampleForecast