code: def train(
      self,
      data: TrainingData,
      iterations: int = 1,
      num_samples: int = 0,
  ) -> None:
      logging.info("LogisticRegressionTrainer.train...")
      self._model = None
      best_score = float("-inf")
      for _ in range(iterations):
          x, y, sw = super()._sample(
              data.train_x,
              data.train_y,
              data.train_weight,
              num_samples,
              True,
          )
          sx, sy, ssw = super()._sample(
              data.validation_x,
              data.validation_y,
              data.validation_weight,
          )
          for c in np.logspace(-5, 4, num=10, base=10):
              model = LogisticRegression(
                  C=c,
                  fit_intercept=False,
                  solver=self._solver,
                  max_iter=1000,
                  multi_class="auto",
                  n_jobs=-1,
              )
              model.fit(x, y, sw)
              score = model.score(sx, sy, ssw)
              logging.info(f"  C: {c}, score: {score}")
              if score > best_score:
                  best_score = score
                  self._model = model
  
imports: import logging
  import math
  import time
  from typing import Optional
  import numpy
  import torch
  from reagent.ope.estimators.types import PredictResults, Trainer, TrainingData
  from sklearn.linear_model import Lasso, LogisticRegression, SGDClassifier
  from sklearn.metrics import accuracy_score, mean_squared_error
  from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
  from torch import Tensor
label_desc: Use image node_id 3 for calling the LogisticRegressionTrainer obj's underlying member method code with example usage: obj.train(data, iterations, num_samples) without return types