class_docstr: Generic vision network.
code: def __init__(
      self, obs_space, action_space, num_outputs, model_config, name
  ):
      TorchModelV2.__init__(
          self, obs_space, action_space, num_outputs, model_config, name
      )
      nn.Module.__init__(self)
  
      self.device = torch.device(
          "cuda" if torch.cuda.is_available() else "cpu"
      )
  
      self.layer_1 = nn.Linear(obs_space.shape[0], 16).to(self.device)
      self.layer_out = nn.Linear(16, num_outputs).to(self.device)
      self.value_branch = nn.Linear(16, 1).to(self.device)
      self.cur_value = None
  
imports: import h5py
  import numpy
  from pathlib import Path
  import unittest
  import ray
  from ray.rllib.algorithms.ppo import PPOConfig
  from ray.rllib.models.catalog import ModelCatalog
  from ray.rllib.models.tf.misc import normc_initializer
  from ray.rllib.models.tf.tf_modelv2 import TFModelV2
  from ray.rllib.models.torch.torch_modelv2 import TorchModelV2
  from ray.rllib.policy.sample_batch import DEFAULT_POLICY_ID
  from ray.rllib.utils.framework import try_import_tf, try_import_torch
  from ray.rllib.utils.test_utils import check, framework_iterator
label_desc: Use image node_id 1 to create a new MyTorchModel object from inherited base classes: TorchModelV2, nn with example: obj = MyTorchModel(obs_space, action_space, num_outputs, model_config, name)