code: def __init__(
      self,
      input_size: int = None,
      output_size: int = None,
      shape: List[int] = None,
      max_params: int = int(3e5),
      device: str = "",
  ) -> None:
      super(ResidualNet, self).__init__()
      self.net = torch.nn.Sequential(
          *(
              [ResidualModule(input_size) for _ in range(1)]
              + [
                  nn.Linear(
                      input_size,
                      max([input_size * 2, output_size * 2, 400]),
                  ),
                  nn.Linear(
                      max([input_size * 2, output_size * 2, 400]),
                      output_size,
                  ),
              ]
          )
      )
  
      if device == "":
          device, available_devices = get_devices()
      else:
          device = get_device_from_name(device)
          available_devices = 0
      self.to(device, available_devices)
  
file_path: lightwood/lightwood/mixer/helpers/residual_net.py
imports: from typing import List
  import torch
  from torch import nn
  from lightwood.helpers.torch import LightwoodAutocast
  from lightwood.helpers.device import get_device_from_name, get_devices
  from lightwood.helpers.log import log
label_desc: Use image node_id 1 to create a new ResidualNet object from inherited base classes: torch.nn with example: obj = ResidualNet(input_size, output_size, shape, max_params, device)