code: def __init__(
      self, input_shape: tp.Tuple[int, ...], output_size: int
  ) -> None:
      super().__init__()  # type: ignore
      assert len(input_shape) == 1
      self.lin1 = nn.Linear(input_shape[0], 16)  # type: ignore
      self.lin2 = nn.Linear(16, 16)  # type: ignore
      self.lin3 = nn.Linear(16, 16)  # type: ignore
      self.head = nn.Linear(16, output_size)
  
imports: import warnings
  import operator
  import copy
  import typing
  import gym
  import numpy
  from nevergrad.common.tools import pytorch_import_fix
  from nevergrad.parametrization import parameter
  from ..base import ExperimentFunction
  from .None import base
  from .None import envs
  import torch
  import torch.nn.functional
  from torch import nn
  from torch.utils.data import WeightedRandomSampler
label_desc: Use image node_id 1 to create a new DenseNet object from inherited base classes: nn with example: obj = DenseNet(input_shape, output_size)