code: def push_and_pull(
      self,
      history,
      x,
      batch_size=None,
      n_id=None,
      offset=None,
      count=None,
  ):
      if len(self.histories) == 0:
          return x
  
      if n_id is None:
          return x
  
      assert n_id is not None
      assert batch_size is not None, "batch_size should not be None"
  
      if batch_size == len(n_id):
          if self._async:
              self.pool.async_push(
                  x[:batch_size], history.emb, offset, count
              )
          return x
  
      if self._async:
          out_batch_size = len(n_id) - batch_size
          out = self.pool.sync_pull()[:out_batch_size]
          self.pool.async_push(
              x[:batch_size], history.emb, offset, count
          )
          out = paddle.concat([x[:batch_size], out], axis=0)
          self.pool.free_pull()
          return out
      else:
          # TODO(daisiming): Due to some limitations of Paddle OP, we leave here as a todo.
          return x
  
file_path: PGL/apps/GNNAutoScale/gnn_models/base_model.py
imports: import os
  import sys
  import numpy
  import paddle
  from pgl.utils.stream_pool import StreamPool, async_write
  from history import History
  from utils import process_batch_data, check_device
label_desc: Use image node_id 5 for calling the ScalableGNN obj's underlying member method code with example usage: obj.push_and_pull(history, x, batch_size, n_id, offset, count) and returns: x, x, x, out, x