class_docstr: DefaultNet variant that adds a secondary stream (simple linear layer) with constrained
  weights to learn autoregressive coefficients for numerical time series targets.
code: def to(self, device=None, available_devices=None):
      if self.ar_net:
          self.ar_net.to(device)
      return super().to(device)
  
file_path: lightwood/lightwood/mixer/helpers/ar_net.py
imports: import torch
  from torch import nn
  from lightwood.mixer.helpers.default_net import DefaultNet
  from lightwood.helpers.torch import LightwoodAutocast
label_desc: Use image node_id 2 for calling the ArNet obj's underlying member method code with example usage: obj.to(device, available_devices) and returns: super