class_docstr: An `Estimator` that computes the mean targets in the training data, in the
  trailing `prediction_length` observations, and produces a
  `ConstantPredictor` that always predicts such mean value.
  
  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: PositiveInt,
      num_samples: PositiveInt,
  ) -> None:
      super().__init__()
      self.prediction_length = prediction_length
      self.num_samples = num_samples
  
imports: from typing import Optional
  import numpy
  from gluonts.core.component import validated
  from gluonts.dataset.common import DataEntry, Dataset
  from gluonts.dataset.field_names import FieldName
  from gluonts.dataset.util import forecast_start
  from gluonts.model.estimator import Estimator
  from gluonts.model.forecast import SampleForecast
  from gluonts.model.predictor import RepresentablePredictor
  from gluonts.model.trivial.constant import ConstantPredictor
  from gluonts.pydantic import PositiveInt
label_desc: Use image node_id 1 to create a new MeanEstimator object from inherited base classes: Estimator with example: obj = MeanEstimator(prediction_length, num_samples)