class_docstr: An example for a non-Keras ModelV2 in tf that learns a single weight.
  
  Defines all network architecture in `forward` (not `__init__` as it's
  usually done for Keras-style TFModelV2s).
class_docstr_tok: ["An","example","for","a","non-Keras","ModelV2","in","tf","that","learns","a","single","weight",".","Defines","all","network","architecture","in","`","forward","`","(","not","`","__init__","`","as","it's","usually","done","for","Keras-style","TFModelV2s",")","."]
code: def value_function(self):
      return tf.reshape(self._value_out, [-1])
  
file_path: ray/rllib/examples/models/fast_model.py
imports: from ray.rllib.models.modelv2 import ModelV2
  from ray.rllib.models.tf.tf_modelv2 import TFModelV2
  from ray.rllib.models.torch.misc import SlimFC
  from ray.rllib.models.torch.torch_modelv2 import TorchModelV2
  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 3 for calling the FastModel obj's underlying member method code with example usage: obj.value_function() and returns: tf