parameters: (self, key, out=None, priority=0, row_ids=None)
docstring: Pulls a single RowSparseNDArray value or a sequence of RowSparseNDArray values \
          from the store with specified row_ids.
  
          `row_sparse_pull` is executed asynchronously after all previous
          `pull`/`row_sparse_pull` calls and the last `push` call for the
          same input key(s) are finished.
  
          The returned values are guaranteed to be the latest values in the store.
  
          Parameters
          ----------
          key : str, int, or sequence of str or int
              Keys.
  
          out: RowSparseNDArray or list of RowSparseNDArray or list of list of RowSparseNDArray
              Values corresponding to the keys. The stype is expected to be row_sparse
  
          priority : int, optional
              The priority of the pull operation.
              Higher priority pull operations are likely to be executed before
              other pull actions.
  
          row_ids : NDArray or list of NDArray
              The row_ids for which to pull for each value. Each row_id is an 1D NDArray \
              whose values don't have to be unique nor sorted.
  
          Examples
          --------
          >>> shape = (3, 3)
          >>> kv.init('3', mx.nd.ones(shape).tostype('row_sparse'))
          >>> a = mx.nd.sparse.zeros('row_sparse', shape)
          >>> row_ids = mx.nd.array([0, 2], dtype='int64')
          >>> kv.row_sparse_pull('3', out=a, row_ids=row_ids)
          >>> print a.asnumpy()
          [[ 1.  1.  1.]
          [ 0.  0.  0.]
          [ 1.  1.  1.]]
          >>> duplicate_row_ids = mx.nd.array([2, 2], dtype='int64')
          >>> kv.row_sparse_pull('3', out=a, row_ids=duplicate_row_ids)
          >>> print a.asnumpy()
          [[ 0.  0.  0.]
          [ 0.  0.  0.]
          [ 1.  1.  1.]]
          >>> unsorted_row_ids = mx.nd.array([1, 0], dtype='int64')
          >>> kv.row_sparse_pull('3', out=a, row_ids=unsorted_row_ids)
          >>> print a.asnumpy()
          [[ 1.  1.  1.]
          [ 1.  1.  1.]
          [ 0.  0.  0.]]
docstring_summary: Pulls a single RowSparseNDArray value or a sequence of RowSparseNDArray values \
          from the store with specified row_ids.
docstring_tokens: Pulls a single RowSparseNDArray value or a sequence of RowSparseNDArray values \ from the store with specified row_ids .
function: def row_sparse_pull(self, key, out=None, priority=0, row_ids=None):
          """ Pulls a single RowSparseNDArray value or a sequence of RowSparseNDArray values \
          from the store with specified row_ids.
  
          `row_sparse_pull` is executed asynchronously after all previous
          `pull`/`row_sparse_pull` calls and the last `push` call for the
          same input key(s) are finished.
  
          The returned values are guaranteed to be the latest values in the store.
  
          Parameters
          ----------
          key : str, int, or sequence of str or int
              Keys.
  
          out: RowSparseNDArray or list of RowSparseNDArray or list of list of RowSparseNDArray
              Values corresponding to the keys. The stype is expected to be row_sparse
  
          priority : int, optional
              The priority of the pull operation.
              Higher priority pull operations are likely to be executed before
              other pull actions.
  
          row_ids : NDArray or list of NDArray
              The row_ids for which to pull for each value. Each row_id is an 1D NDArray \
              whose values don't have to be unique nor sorted.
  
          Examples
          --------
          >>> shape = (3, 3)
          >>> kv.init('3', mx.nd.ones(shape).tostype('row_sparse'))
          >>> a = mx.nd.sparse.zeros('row_sparse', shape)
          >>> row_ids = mx.nd.array([0, 2], dtype='int64')
          >>> kv.row_sparse_pull('3', out=a, row_ids=row_ids)
          >>> print a.asnumpy()
          [[ 1.  1.  1.]
          [ 0.  0.  0.]
          [ 1.  1.  1.]]
          >>> duplicate_row_ids = mx.nd.array([2, 2], dtype='int64')
          >>> kv.row_sparse_pull('3', out=a, row_ids=duplicate_row_ids)
          >>> print a.asnumpy()
          [[ 0.  0.  0.]
          [ 0.  0.  0.]
          [ 1.  1.  1.]]
          >>> unsorted_row_ids = mx.nd.array([1, 0], dtype='int64')
          >>> kv.row_sparse_pull('3', out=a, row_ids=unsorted_row_ids)
          >>> print a.asnumpy()
          [[ 1.  1.  1.]
          [ 1.  1.  1.]
          [ 0.  0.  0.]]
          """
          assert(out is not None)
          assert(row_ids is not None)
          ckeys, cvals, use_str_keys = _ctype_key_value(key, out)
          _, crow_ids, _ = _ctype_key_value(key, row_ids)
          assert(len(crow_ids) == len(cvals)), \
                 "the number of row_ids doesn't match the number of values"
          if use_str_keys:
              check_call(_LIB.MXKVStorePullRowSparseEx(
                  self.handle, mx_uint(len(ckeys)), ckeys, cvals, crow_ids, ctypes.c_int(priority)))
          else:
              check_call(_LIB.MXKVStorePullRowSparse(
                  self.handle, mx_uint(len(ckeys)), ckeys, cvals, crow_ids, ctypes.c_int(priority)))
function_tokens: def row_sparse_pull ( self , key , out = None , priority = 0 , row_ids = None ) : assert ( out is not None ) assert ( row_ids is not None ) ckeys , cvals , use_str_keys = _ctype_key_value ( key , out ) _ , crow_ids , _ = _ctype_key_value ( key , row_ids ) assert ( len ( crow_ids ) == len ( cvals ) ) , "the number of row_ids doesn't match the number of values" if use_str_keys : check_call ( _LIB . MXKVStorePullRowSparseEx ( self . handle , mx_uint ( len ( ckeys ) ) , ckeys , cvals , crow_ids , ctypes . c_int ( priority ) ) ) else : check_call ( _LIB . MXKVStorePullRowSparse ( self . handle , mx_uint ( len ( ckeys ) ) , ckeys , cvals , crow_ids , ctypes . c_int ( priority ) ) )