class_docstr: The heterogeneous compitable GraphSAGE operator is derived from the `"Inductive Representation
  Learning on Large Graphs" <https://arxiv.org/abs/1706.02216>`_, `"Modeling polypharmacy side
  effects with graph convolutional networks" <https://arxiv.org/abs/1802.00543>`_, and `"Modeling
  Relational Data with Graph Convolutional Networks" <https://arxiv.org/abs/1703.06103>`_ papers.
  
  .. note::
  
      This layer is usually used with the :class:`HeteroConv`.
  
  Args:
      in_channels_neigh (int): The input dimension of the neighbor node type.
      out_channels (int): The dimension of the output.
      in_channels_self (int): The input dimension of the self node type.
          Default is `None` where the `in_channels_self` is equal to `in_channels_neigh`.
      remove_self_loop (bool): Whether to remove self loops using :class:`torch_geometric.utils.remove_self_loops`.
          Default is `True`.
docstr: This function basically fuses the :meth:`message` and :meth:`aggregate` into 
  one function. It will save memory and avoid message materialization. More 
  information please refer to the PyTorch Geometric documentation.
  
  Args:
      edge_index (:class:`torch_sparse.SparseTensor`): The `edge_index` sparse tensor.
      node_feature_neigh (:class:`torch.Tensor`): Neighbor feature tensor.
docstr_tok: ["This","function","basically","fuses","the",":","meth",":","`","message","`","and",":","meth",":","`","aggregate","`","into","one","function",".","It","will","save","memory","and","avoid","message","materialization",".","More","information","please","refer","to","the","PyTorch","Geometric","documentation",".","Args",":","edge_index","(",":","class",":","`","torch_sparse.SparseTensor","`",")",":","The","`","edge_index","`","sparse","tensor",".","node_feature_neigh","(",":","class",":","`","torch.Tensor","`",")",":","Neighbor","feature","tensor","."]
code: def message_and_aggregate(self, edge_index, node_feature_neigh):
      r"""
      This function basically fuses the :meth:`message` and :meth:`aggregate` into
      one function. It will save memory and avoid message materialization. More
      information please refer to the PyTorch Geometric documentation.
  
      Args:
          edge_index (:class:`torch_sparse.SparseTensor`): The `edge_index` sparse tensor.
          node_feature_neigh (:class:`torch.Tensor`): Neighbor feature tensor.
      """
      out = matmul(edge_index, node_feature_neigh, reduce="mean")
      return out
  
imports: import collections.abc
  import torch
  import torch_geometric.nn
  import torch_geometric.utils
  import torch.nn
  from torch import Tensor
  from torch_geometric.nn.inits import reset
  from torch_sparse import matmul
  from typing import List, Dict
label_desc: Use image node_id 4 for calling the HeteroSAGEConv obj's underlying member method code with example usage: obj.message_and_aggregate(edge_index, node_feature_neigh) and returns: out