code: def forward(self, *args: tp.Any) -> tp.Any:
      assert len(args) == 1
      x = F.relu(self.lin1(args[0]))
      x = F.relu(self.lin2(x))
      x = F.relu(self.lin3(x))
      return self.head(x)
  
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 2 for calling the DenseNet obj's underlying member method code with example usage: obj.forward() and returns: self