class_docstr: The Direct Method estimator.
  
  Let s_t, a_t, and r_t be the state, action, and reward at timestep t.
  
  This method trains a Q-model for the evaluation policy \pi_e on behavior
  data generated by \pi_b. Currently, RLlib implements this using
  Fitted-Q Evaluation (FQE). You can also implement your own model
  and pass it in as `q_model_config = {"type": your_model_class, **your_kwargs}`.
  
  This estimator computes the expected return for \pi_e for an episode as:
  V^{\pi_e}(s_0) = \sum_{a \in A} \pi_e(a | s_0) Q(s_0, a)
  and returns the mean and standard deviation over episodes.
  
  For more information refer to https://arxiv.org/pdf/1911.06854.pdf
docstr: Trains self.model on the given batch.
  
  Args:
      batch: A SampleBatchType to train on
  
  Returns:
      A dict with key "loss" and value as the mean training loss.
docstr_tok: ["Trains","self.model","on","the","given","batch",".","Args",":","batch",":","A","SampleBatchType","to","train","on","Returns",":","A","dict","with","key","``","loss","''","and","value","as","the","mean","training","loss","."]
code: def train(self, batch: SampleBatchType) -> Dict[str, Any]:
      """Trains self.model on the given batch.
  
      Args:
          batch: A SampleBatchType to train on
  
      Returns:
          A dict with key "loss" and value as the mean training loss.
      """
      batch = convert_ma_batch_to_sample_batch(batch)
      losses = self.model.train(batch)
      return {"loss": np.mean(losses)}
  
imports: import logging
  from typing import Dict, Any, Optional, List
  import math
  import numpy
  from ray.data import Dataset
  from ray.rllib.offline.estimators.off_policy_estimator import OffPolicyEstimator
  from ray.rllib.offline.offline_evaluation_utils import compute_q_and_v_values
  from ray.rllib.offline.offline_evaluator import OfflineEvaluator
  from ray.rllib.offline.estimators.fqe_torch_model import FQETorchModel
  from ray.rllib.policy import Policy
  from ray.rllib.policy.sample_batch import convert_ma_batch_to_sample_batch
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.utils.annotations import DeveloperAPI, override
  from ray.rllib.utils.typing import SampleBatchType
  from ray.rllib.utils.numpy import convert_to_numpy
label_desc: Use image node_id 5 for calling the DirectMethod obj's underlying member method code with example usage: obj.train(batch) and returns: dict