class_docstr: Model that handles simple discrete action masking.
  
  This assumes the outputs are logits for a single Categorical action dist.
  Getting this to work with a more complex output (e.g., if the action space
  is a tuple of several distributions) is also possible but left as an
  exercise to the reader.
class_docstr_tok: ["Model","that","handles","simple","discrete","action","masking",".","This","assumes","the","outputs","are","logits","for","a","single","Categorical","action","dist",".","Getting","this","to","work","with","a","more","complex","output","(","e.g.",",","if","the","action","space","is","a","tuple","of","several","distributions",")","is","also","possible","but","left","as","an","exercise","to","the","reader","."]
code: def value_function(self):
      return self.internal_model.value_function()
  
file_path: ray/python/ray/rllib/examples/models/action_mask_model.py
imports: from gymnasium.spaces import Dict
  from ray.rllib.models.tf.fcnet import FullyConnectedNetwork
  from ray.rllib.models.tf.tf_modelv2 import TFModelV2
  from ray.rllib.models.torch.torch_modelv2 import TorchModelV2
  from ray.rllib.models.torch.fcnet import FullyConnectedNetwork
  from ray.rllib.utils.framework import try_import_tf, try_import_torch
  from ray.rllib.utils.torch_utils import FLOAT_MIN
label_desc: Use image node_id 3 for calling the ActionMaskModel obj's underlying member method code with example usage: obj.value_function() and returns: self