code: def _train_model(
      self, samples: Sequence[LogSample], force_train: bool = False
  ) -> bool:
      if self._trainer is None:
          logger.error("Target model trainer not set")
          return False
      trainer = self._trainer
      assert trainer is not None
      if trainer.is_trained and not force_train:
          return True
      logger.info("  training direct model...")
      st = time.perf_counter()
      sample_size = len(samples)
      training_size = int(
          sample_size * DMEstimator.TRAINING_VALIDATION_SPLIT
      )
      train_x = []
      train_y = []
      for i in range(training_size):
          sample = samples[i]
          if sample.item_feature is None:
              continue
          train_x.append(
              torch.cat(
                  (
                      torch.tensor(
                          sample.log_action.value, dtype=torch.float
                      ).flatten(),
                      sample.item_feature.flatten(),
                  )
              )
          )
          train_y.append(sample.log_reward)
      if len(train_x) == 0:
          logger.error(
              "Item features not provided, DM is not available"
          )
          return False
      train_x = torch.stack(train_x)
      train_y = torch.tensor(
          train_y, dtype=torch.float, device=train_x.device
      )
      vali_x = []
      vali_y = []
      for i in range(training_size, sample_size):
          sample = samples[i]
          if sample.item_feature is None:
              continue
          vali_x.append(
              torch.cat(
                  (
                      torch.tensor(
                          sample.log_action.value, dtype=torch.float
                      ).flatten(),
                      sample.item_feature.flatten(),
                  )
              )
          )
          vali_y.append(sample.log_reward)
      if len(vali_x) == 0:
          vali_x = train_x.detach().clone()
          vali_y = train_y.detach().clone()
      else:
          vali_x = torch.stack(vali_x)
          vali_y = torch.tensor(
              vali_y, dtype=torch.float, device=vali_x.device
          )
      training_data = TrainingData(
          train_x, train_y, None, vali_x, vali_y, None
      )
      trainer.train(training_data)
      logger.info(
          f"  training direct model done: {time.perf_counter() - st}s"
      )
      return True
  
imports: import logging
  import time
  from abc import ABC, abstractmethod
  from dataclasses import dataclass
  from typing import Optional, Sequence, Tuple, Union
  import numpy
  import torch
  from reagent.ope.estimators.estimator import Estimator, EstimatorResult
  from reagent.ope.estimators.types import Action, ActionDistribution, ActionSpace, Reward, Trainer, TrainingData, Values
  from reagent.ope.utils import Clamper, RunningAverage
  from torch import Tensor
label_desc: Use image node_id 2 for calling the DMEstimator obj's underlying member method code with example usage: obj._train_model(samples, force_train) and returns: True, False, True, False