code: def __init__(self, dim_input, dim_model):
      super().__init__()
      self.net = nn.Sequential(
          torch.nn.Linear(dim_input, dim_model),
          torch.nn.ReLU(),
          torch.nn.Linear(dim_model, 1),
      )
      for p in self.parameters():
          if p.dim() > 1:
              nn.init.uniform_(p, -3, 3)
  
imports: import random
  import unittest
  from collections import defaultdict
  from typing import Dict
  import nevergrad
  import numpy
  import torch
  import torch.nn
  from reagent.lite.optimizer import BayesianByBackpropOptimizer, BayesianMLPEnsemblerOptimizer, BayesianOptimizerBase, GREEDY_TEMP, GumbelSoftmaxOptimizer, NeverGradOptimizer, PolicyGradientOptimizer, QLearningOptimizer, RandomSearchOptimizer, sol_to_tensors
label_desc: Use image node_id 1 to create a new GroundTruthNet object from inherited base classes: nn with example: obj = GroundTruthNet(dim_input, dim_model)