code: def _get_median(data, n_zeros):
      """Compute the median of data with n_zeros additional zeros.
  
      This function is used to support sparse matrices; it modifies data
      in-place.
      """
      n_elems = len(data) + n_zeros
      if not n_elems:
          return np.nan
      n_negative = np.count_nonzero(data < 0)
      middle, is_odd = divmod(n_elems, 2)
      data.sort()
  
      if is_odd:
          return _get_elem_at_rank(middle, data, n_negative, n_zeros)
  
      return (
          _get_elem_at_rank(middle - 1, data, n_negative, n_zeros)
          + _get_elem_at_rank(middle, data, n_negative, n_zeros)
      ) / 2.0
  
imports: import numpy
  import scipy.sparse
  from ..utils.validation import _check_sample_weight
  from .sparsefuncs_fast import csc_mean_variance_axis0
  from .sparsefuncs_fast import csr_mean_variance_axis0
  from .sparsefuncs_fast import incr_mean_variance_axis0
label_desc: Use image node_id 20 for calling a global function with example usage: _get_median(data, n_zeros) and returns: unknown, np, _get_elem_at_rank