class_docstr: Random forest attacker.
docstr: Setup a random forest pipeline with cross-validation.
code: def train_model(
      self, input_features, is_training_labels, sample_weight=None
  ):
      """Setup a random forest pipeline with cross-validation."""
      with self.ctx_mgr:
          rf_model = ensemble.RandomForestClassifier(n_jobs=self.n_jobs)
  
          param_grid = {
              "n_estimators": [100],
              "max_features": ["auto", "sqrt"],
              "max_depth": [5, 10, 20, None],
              "min_samples_split": [2, 5, 10],
              "min_samples_leaf": [1, 2, 4],
          }
          model = model_selection.GridSearchCV(
              rf_model,
              param_grid=param_grid,
              cv=3,
              n_jobs=self.n_jobs,
              verbose=0,
          )
          model.fit(
              input_features,
              is_training_labels,
              sample_weight=sample_weight,
          )
      self.model = model
  
imports: import contextlib
  import dataclasses
  import logging
  from typing import Optional
  import numpy
  from sklearn import ensemble
  from sklearn import linear_model
  from sklearn import model_selection
  from sklearn import neighbors
  from sklearn import neural_network
  from sklearn.utils import parallel_backend
  from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack import data_structures
label_desc: Use image node_id 2 for calling the RandomForestAttacker obj's underlying member method code with example usage: obj.train_model(input_features, is_training_labels, sample_weight) without return types