class_docstr: base matrix class for compressed row and column oriented matrices
class_docstr_tok: ["base","matrix","class","for","compressed","row","and","column","oriented","matrices"]
docstr: Sets value at each (i, j) to zero, preserving sparsity structure.
  
  Here (i,j) index major and minor respectively.
code: def _zero_many(self, i, j):
      """Sets value at each (i, j) to zero, preserving sparsity structure.
  
      Here (i,j) index major and minor respectively.
      """
      i, j, M, N = self._prepare_indices(i, j)
  
      n_samples = len(i)
      offsets = np.empty(n_samples, dtype=self.indices.dtype)
      ret = _sparsetools.csr_sample_offsets(
          M, N, self.indptr, self.indices, n_samples, i, j, offsets
      )
      if ret == 1:
          # rinse and repeat
          self.sum_duplicates()
          _sparsetools.csr_sample_offsets(
              M, N, self.indptr, self.indices, n_samples, i, j, offsets
          )
  
      # only assign zeros to the existing sparsity structure
      self.data[offsets[offsets > -1]] = 0
  
imports: from __future__ import division, print_function, absolute_import
  from warnings import warn
  import operator
  import numpy
  from scipy._lib.six import zip
  from scipy._lib._util import _prune_array
  from .base import spmatrix, isspmatrix, SparseEfficiencyWarning
  from .data import _data_matrix, _minmax_mixin
  from .dia import dia_matrix
  from .None import _sparsetools
  from .sputils import upcast, upcast_char, to_native, isdense, isshape, getdtype, isscalarlike, IndexMixin, get_index_dtype, downcast_intp_index, get_sum_dtype, check_shape, matrix, asmatrix
label_desc: Use image node_id 30 for calling the _cs_matrix obj's underlying member method code with example usage: obj._zero_many(i, j) without return types