code: def _generate_bagging_indices(
      random_state,
      bootstrap_features,
      bootstrap_samples,
      n_features,
      n_samples,
      max_features,
      max_samples,
  ):
      """Randomly draw feature and sample indices."""
      # Get valid random state
      random_state = check_random_state(random_state)
  
      # Draw indices
      feature_indices = _generate_indices(
          random_state, bootstrap_features, n_features, max_features
      )
      sample_indices = _generate_indices(
          random_state, bootstrap_samples, n_samples, max_samples
      )
  
      return feature_indices, sample_indices
  
imports: import itertools
  import numbers
  from abc import ABCMeta, abstractmethod
  from functools import partial
  from numbers import Integral
  from warnings import warn
  import numpy
  from ..base import ClassifierMixin, RegressorMixin, _fit_context
  from ..metrics import accuracy_score, r2_score
  from ..tree import DecisionTreeClassifier, DecisionTreeRegressor
  from ..utils import check_random_state, column_or_1d, indices_to_mask
  from ..utils._param_validation import HasMethods, Interval, RealNotInt, StrOptions
  from ..utils._tags import _safe_tags
  from ..utils.metaestimators import available_if
  from ..utils.multiclass import check_classification_targets
  from ..utils.parallel import Parallel, delayed
  from ..utils.random import sample_without_replacement
  from ..utils.validation import _check_sample_weight, check_is_fitted, has_fit_parameter
  from ._base import BaseEnsemble, _partition_estimators
label_desc: Use image node_id 2 for calling a global function with example usage: _generate_bagging_indices(random_state, bootstrap_features, bootstrap_samples, n_features, n_samples, max_features, max_samples) and returns: feature_indices,  sample_indices