class_docstr: A conv. + recurrent torch net example using a pre-trained MobileNet.
class_docstr_tok: ["A","conv",".","+","recurrent","torch","net","example","using","a","pre-trained","MobileNet","."]
code: def value_function(self):
      assert self._features is not None, "must call forward() first"
      return torch.reshape(self.value_branch(self._features), [-1])
  
file_path: ray/python/ray/rllib/examples/models/mobilenet_v2_with_lstm_models.py
imports: import numpy
  from ray.rllib.models.modelv2 import ModelV2
  from ray.rllib.models.tf.recurrent_net import RecurrentNetwork
  from ray.rllib.models.torch.misc import SlimFC
  from ray.rllib.models.torch.recurrent_net import RecurrentNetwork
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.framework import try_import_tf, try_import_torch
label_desc: Use image node_id 4 for calling the TorchMobileV2PlusRNNModel obj's underlying member method code with example usage: obj.value_function() and returns: torch