class_docstr: Generic vision network.
code: def forward(self, input_dict, state, seq_lens):
      layer_1_out = self.layer_1(input_dict["obs"])
      logits = self.layer_out(layer_1_out)
      self.cur_value = self.value_branch(layer_1_out).squeeze(1)
      return logits, state
  
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 2 for calling the MyTorchModel obj's underlying member method code with example usage: obj.forward(input_dict, state, seq_lens) and returns: logits,  state