code: def _mul_multivector(self, other):
      result_dtype = upcast_char(self.dtype.char, other.dtype.char)
      if self.ndim == 2:
          result_shape = (other.shape[1], self.shape[0])
          col = self.col
          row = self.row
      elif self.ndim == 1:
          result_shape = (other.shape[1],)
          col = self.indices[0]
          row = np.zeros_like(col)
      else:
          raise NotImplementedError(
              f"coo_matvec not implemented for ndim={self.ndim}"
          )
  
      result = np.zeros(result_shape, dtype=result_dtype)
      for i, other_col in enumerate(other.T):
          coo_matvec(
              self.nnz,
              row,
              col,
              self.data,
              other_col,
              result[i : i + 1],
          )
      return result.T.view(type=type(other))
  
imports: import math
  from warnings import warn
  import numpy
  from ._matrix import spmatrix
  from ._sparsetools import coo_tocsr, coo_todense, coo_matvec
  from ._base import issparse, SparseEfficiencyWarning, _spbase, sparray
  from ._data import _data_matrix, _minmax_mixin
  from ._sputils import upcast, upcast_char, to_native, isshape, getdtype, getdata, downcast_intp_index, get_index_dtype, check_shape, check_reshape_kwargs
  import operator
label_desc: Use image node_id 25 for calling the _coo_base obj's underlying member method code with example usage: obj._mul_multivector(other) and returns: result