code: def axis0_safe_slice(X, mask, len_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.
      """
      if len_mask != 0:
          return X[safe_mask(X, mask), :]
      return np.zeros(shape=(0, X.shape[1]))
  
imports: from collections import Sequence
  import numpy
  from scipy.sparse import issparse
  import warnings
  from .murmurhash import murmurhash3_32
  from .validation import as_float_array, assert_all_finite, check_random_state, column_or_1d, check_array, check_consistent_length, check_X_y, indexable, check_symmetric
  from .deprecation import deprecated
  from .class_weight import compute_class_weight, compute_sample_weight
  from ..externals.joblib import cpu_count
  from ..exceptions import ConvergenceWarning
  from ..exceptions import DataConversionWarning
label_desc: Use image node_id 2 for calling a global function with example usage: axis0_safe_slice(X, mask, len_mask) and returns: np, X