parameters: (self)
return_statement: return nodes
docstring: Get batched sampled nodes.
  
      Return:
        A `Nodes` object, shape=[`batch_size`]
      Raise:
        `graphlearn.OutOfRangeError`
docstring_summary: Get batched sampled nodes.
docstring_tokens: Get batched sampled nodes .
function: def get(self):
      """ Get batched sampled nodes.
  
      Return:
        A `Nodes` object, shape=[`batch_size`]
      Raise:
        `graphlearn.OutOfRangeError`
      """
      mask_type = utils.get_mask_type(self._type, self._mask)
      state = self._graph.node_state.get(mask_type)
      req = pywrap.new_get_nodes_request(mask_type,
                                         self._strategy,
                                         self._node_from,
                                         self._batch_size,
                                         state)
  
      res = pywrap.new_get_nodes_response()
      status = self._client.get_nodes(req, res)
      if not status.ok():
        self._graph.node_state.inc(mask_type)
      else:
        ids = pywrap.get_node_ids(res)
  
      pywrap.del_op_response(res)
      pywrap.del_op_request(req)
      errors.raise_exception_on_not_ok_status(status)
  
      nodes = self._graph.get_nodes(self._node_type, ids)
      return nodes
function_tokens: def get ( self ) : mask_type = utils . get_mask_type ( self . _type , self . _mask ) state = self . _graph . node_state . get ( mask_type ) req = pywrap . new_get_nodes_request ( mask_type , self . _strategy , self . _node_from , self . _batch_size , state ) res = pywrap . new_get_nodes_response ( ) status = self . _client . get_nodes ( req , res ) if not status . ok ( ) : self . _graph . node_state . inc ( mask_type ) else : ids = pywrap . get_node_ids ( res ) pywrap . del_op_response ( res ) pywrap . del_op_request ( req ) errors . raise_exception_on_not_ok_status ( status ) nodes = self . _graph . get_nodes ( self . _node_type , ids ) return nodes