code: def _select_neighbor_and_aggregate(feature, index):
      """The basic operation in message passing.
  
      Caution: the index_selec_ND would cause the reproducibility issue when performing the training on CUDA.
  
      The operation is like map and reduce. `index_select_nd` maps message features to bonds/atoms and
      this method aggregates the results by using `sum` as pooling operation. We can also add a configuration
      here to use `mean` as pooling operation but it is left to future implementation.
  
      References
      ----------
      See: https://pytorch.org/docs/stable/notes/randomness.html
  
      Parameters
      ----------
      feature: np.array
          The candidate feature for aggregate. (n_nodes, hidden)
      index: np.array
          The selected index (neighbor indexes).
  
      Returns
      -------
      None
      """
      neighbor = _index_select_nd(feature, index)
      return neighbor.sum(dim=1)
  
imports: from typing import List, Dict
  import numpy
  from scipy import stats
  from deepchem.models.torch_models.readout import GroverReadout
  from deepchem.models.torch_models.layers import SublayerConnection, PositionwiseFeedForward
label_desc: Use image node_id 2 for calling a global function with example usage: _select_neighbor_and_aggregate(feature, index) and returns: neighbor