class_docstr: Interface for an off policy estimator for counterfactual evaluation.
class_docstr_tok: ["Interface","for","an","off","policy","estimator","for","counterfactual","evaluation","."]
docstr: Train a model for Off-Policy Estimation.
  
  Args:
      batch: SampleBatch to train on
  
  Returns:
      Any optional metrics to return from the estimator
docstr_tok: ["Train","a","model","for","Off-Policy","Estimation",".","Args",":","batch",":","SampleBatch","to","train","on","Returns",":","Any","optional","metrics","to","return","from","the","estimator"]
code: def train(self, batch: SampleBatchType) -> Dict[str, Any]:
      """Train a model for Off-Policy Estimation.
  
      Args:
          batch: SampleBatch to train on
  
      Returns:
          Any optional metrics to return from the estimator
      """
      return {}
  
imports: import gymnasium
  import numpy
  import tree
  from typing import Dict, Any, List
  import logging
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.policy import Policy
  from ray.rllib.policy.sample_batch import convert_ma_batch_to_sample_batch
  from ray.rllib.utils.policy import compute_log_likelihoods_from_input_dict
  from ray.rllib.utils.annotations import DeveloperAPI, ExperimentalAPI, OverrideToImplementCustomLogic
  from ray.rllib.utils.deprecation import Deprecated
  from ray.rllib.utils.numpy import convert_to_numpy
  from ray.rllib.utils.typing import TensorType, SampleBatchType
  from ray.rllib.offline.offline_evaluator import OfflineEvaluator
label_desc: Use image node_id 10 for calling the OffPolicyEstimator obj's underlying member method code with example usage: obj.train(batch) and returns: dict