class_docstr: A :class:`Predictor` that predicts the samples based on the mean of the
  last `context_length` elements of the input target.
  
  Parameters
  ----------
  context_length
      Length of the target context used to condition the predictions.
  prediction_length
      Length of the prediction horizon.
  num_samples
      Number of samples to use to construct :class:`SampleForecast` objects
      for every prediction.
code: def predict_item(self, item: DataEntry) -> SampleForecast:
      if self.context_length is not None:
          target = item["target"][-self.context_length :]
      else:
          target = item["target"]
  
      mean = np.nanmean(target)
      std = np.nanstd(target)
      normal = np.random.standard_normal(self.shape)
  
      return SampleForecast(
          samples=std * normal + mean,
          start_date=forecast_start(item),
          item_id=item.get(FieldName.ITEM_ID),
      )
  
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 2 for calling the MeanPredictor obj's underlying member method code with example usage: obj.predict_item(item) and returns: SampleForecast