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 __init__(
      self, obs_space, action_space, num_outputs, model_config, name
  ):
      super().__init__(
          obs_space, action_space, num_outputs, model_config, name
      )
      # Have we registered our vars yet (see `forward`)?
      self._registered = False
  
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 1 to create a new FastModel object from inherited base classes: TFModelV2 with example: obj = FastModel(obs_space, action_space, num_outputs, model_config, name)