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 __init__(self, prediction_length: int, num_samples: int) -> None:
      super().__init__(prediction_length=prediction_length)
  
      assert num_samples > 0, "The value of `num_samples` should be > 0"
  
      self.num_samples = num_samples
  
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 1 to create a new IdentityPredictor object from inherited base classes: RepresentablePredictor with example: obj = IdentityPredictor(prediction_length, num_samples)