code: def initialize_model() -> nn.HybridBlock:
      # dummy training data
      N = 10  # number of time series
      T = 100  # number of timesteps
      prediction_length = 24
      freq = "1H"
      custom_dataset = np.zeros(shape=(N, T))
      start = pd.Period(
          "01-01-2019", freq=freq
      )  # can be different for each time series
      train_ds = ListDataset(
          [
              {"target": x, "start": start}
              for x in custom_dataset[:, :-prediction_length]
          ],
          freq=freq,
      )
      # create a simple model
      estimator = SimpleFeedForwardEstimator(
          num_hidden_dimensions=[10],
          prediction_length=prediction_length,
          context_length=T,
          trainer=Trainer(
              ctx="cpu",
              epochs=1,
              learning_rate=1e-3,
              num_batches_per_epoch=1,
          ),
      )
  
      # train model
      predictor = estimator.train(train_ds)
  
      return predictor.prediction_net
  
file_path: gluonts/test/mx/trainer/test_model_iteration_averaging.py
imports: import math
  import mxnet
  import mxnet.gluon.nn
  import numpy
  import pandas
  import pytest
  from gluonts.dataset.common import ListDataset
  from gluonts.mx import SimpleFeedForwardEstimator
  from gluonts.mx.trainer import Trainer
  from gluonts.mx.trainer.model_iteration_averaging import NTA, Alpha_Suffix
label_desc: Use image node_id 1 for calling a global function with example usage: initialize_model() and returns: predictor