class_docstr: HINT
  
  The Hierarchical Mixture Networks (HINT) are a highly modular framework that
  combines SoTA neural forecast architectures with a task-specialized mixture
  probability and advanced hierarchical reconciliation strategies. This powerful
  combination allows HINT to produce accurate and coherent probabilistic forecasts.
  
  HINT's incorporates a `TemporalNorm` module into any neural forecast architecture,
  the module normalizes inputs into the network's non-linearities operating range
  and recomposes its output's scales through a global skip connection, improving
  accuracy and training robustness. HINT ensures the forecast coherence via bootstrap
  sample reconciliation that restores the aggregation constraints into its base samples.
  
  Available reconciliations:<br>
  - BottomUp<br>
  - MinTraceOLS<br>
  - MinTraceWLS<br>
  - Identity
  
  **Parameters:**<br>
  `h`: int, Forecast horizon. <br>
  `model`: NeuralForecast model, instantiated model class from [architecture collection](https://nixtla.github.io/neuralforecast/models.pytorch.html).<br>
  `S`: np.ndarray, dumming matrix of size (`base`, `bottom`) see HierarchicalForecast's [aggregate method](https://nixtla.github.io/hierarchicalforecast/utils.html#aggregate).<br>
  `reconciliation`: str, HINT's reconciliation method from ['BottomUp', 'MinTraceOLS', 'MinTraceWLS'].<br>
  `alias`: str, optional,  Custom name of the model.<br>
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code: def get_test_size(self):
      return self.model.test_size
  
file_path: neuralforecast/neuralforecast/models/hint.py
imports: from typing import Optional
  import numpy
  import torch
label_desc: Use image node_id 6 for calling the HINT obj's underlying member method code with example usage: obj.get_test_size() and returns: self