code: def safe_mask(X, mask):
      """Return a mask which is safe to use on X.
  
      Parameters
      ----------
      X : {array-like, sparse matrix}
          Data on which to apply mask.
  
      mask : ndarray
          Mask to be used on X.
  
      Returns
      -------
      mask : ndarray
          Array that is safe to use on X.
      """
      mask = np.asarray(mask)
      if np.issubdtype(mask.dtype, np.signedinteger):
          return mask
  
      if hasattr(X, "toarray"):
          ind = np.arange(mask.shape[0])
          mask = ind[mask]
      return mask
  
imports: import math
  import numbers
  import platform
  import struct
  import timeit
  import warnings
  from collections.abc import Sequence
  from contextlib import contextmanager, suppress
  from itertools import compress, islice
  import numpy
  from scipy.sparse import issparse
  from ..None import get_config
  from ..exceptions import DataConversionWarning
  from .None import _joblib, metadata_routing
  from ._bunch import Bunch
  from ._estimator_html_repr import estimator_html_repr
  from ._param_validation import Interval, validate_params
  from .class_weight import compute_class_weight, compute_sample_weight
  from .deprecation import deprecated
  from .discovery import all_estimators
  from .fixes import parse_version, threadpool_info
  from .murmurhash import murmurhash3_32
  from .validation import _is_arraylike_not_scalar, as_float_array, assert_all_finite, check_array, check_consistent_length, check_random_state, check_scalar, check_symmetric, check_X_y, column_or_1d, indexable
label_desc: Use image node_id 2 for calling a global function with example usage: safe_mask(X, mask) and returns: mask, mask