code: def setup(self, config):
      self.model = nn.Sequential(
          nn.Linear(config.get("input_size", 32), 32),
          nn.ReLU(),
          nn.Linear(32, 10),
      )
  
imports: import os
  import torch
  from torch import nn
  from ray import train, tune
  import random
  import os
  import tempfile
  from ray import train, tune
  from ray.train import Checkpoint
label_desc: Use image node_id 1 for calling the MyTrainableClass obj's underlying member method code with example usage: obj.setup(config) without return types