code: def axis0_safe_slice(X, mask, len_mask):
      """Return a mask which is safer to use on X than safe_mask.
  
      This mask is safer than safe_mask since it returns an
      empty array, when a sparse matrix is sliced with a boolean mask
      with all False, instead of raising an unhelpful error in older
      versions of SciPy.
  
      See: https://github.com/scipy/scipy/issues/5361
  
      Also note that we can avoid doing the dot product by checking if
      the len_mask is not zero in _huber_loss_and_gradient but this
      is not going to be the bottleneck, since the number of outliers
      and non_outliers are typically non-zero and it makes the code
      tougher to follow.
  
      Parameters
      ----------
      X : {array-like, sparse matrix}
          Data on which to apply mask.
  
      mask : ndarray
          Mask to be used on X.
  
      len_mask : int
          The length of the mask.
  
      Returns
      -------
      mask : ndarray
          Array that is safe to use on X.
      """
      if len_mask != 0:
          return X[safe_mask(X, mask), :]
      return np.zeros(shape=(0, X.shape[1]))
  
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 3 for calling a global function with example usage: axis0_safe_slice(X, mask, len_mask) and returns: np, X