class_docstr: Pytorch implementation of the Fitted Q-Evaluation (FQE) model from
  https://arxiv.org/abs/1911.06854
class_docstr_tok: ["Pytorch","implementation","of","the","Fitted","Q-Evaluation","(","FQE",")","model","from","https",":","\/\/arxiv.org\/abs\/1911.06854"]
docstr: Creates a FQE Model from a state dict.
  
  Args:
      state: A state dict returned by `get_state`.
  
  Returns:
      An instance of the FQETorchModel.
docstr_tok: ["Creates","a","FQE","Model","from","a","state","dict",".","Args",":","state",":","A","state","dict","returned","by","`","get_state","`",".","Returns",":","An","instance","of","the","FQETorchModel","."]
code: def from_state(cls, state: Dict[str, Any]) -> "FQETorchModel":
      """Creates a FQE Model from a state dict.
  
      Args:
          state: A state dict returned by `get_state`.
  
      Returns:
          An instance of the FQETorchModel.
      """
      policy = Policy.from_state(state["policy_state"])
      model = cls(
          policy=policy,
          gamma=state["gamma"],
          model_config=state["model_config"],
      )
      model.set_state(state)
      return model
  
imports: from typing import Dict, Any
  from ray.rllib.models.utils import get_initializer
  from ray.rllib.policy import Policy
  from ray.rllib.models.catalog import ModelCatalog
  from ray.rllib.models.torch.torch_modelv2 import TorchModelV2
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.utils.annotations import DeveloperAPI
  from ray.rllib.utils.framework import try_import_torch
  from ray.rllib.utils.annotations import is_overridden
  from ray.rllib.utils.typing import ModelConfigDict, TensorType
  from gymnasium.spaces import Discrete
label_desc: Use image node_id 9 for calling the FQETorchModel obj's underlying member method code with example usage: obj.from_state(cls, state) and returns: model