class_docstr: Base class for creating message passing layers of the form
  
  .. math::
      \mathbf{x}_i^{\prime} = \gamma_{\mathbf{\Theta}} \left( \mathbf{x}_i,
      \square_{j \in \mathcal{N}(i)} \, \phi_{\mathbf{\Theta}}
      \left(\mathbf{x}_i, \mathbf{x}_j,\mathbf{e}_{j,i}\right) \right),
  
  where :math:`\square` denotes a differentiable, permutation invariant
  function, *e.g.*, sum, mean or max, and :math:`\gamma_{\mathbf{\Theta}}`
  and :math:`\phi_{\mathbf{\Theta}}` denote differentiable functions such as
  MLPs.
  See `here <https://pytorch-geometric.readthedocs.io/en/latest/notes/
  create_gnn.html>`__ for the accompanying tutorial.
  
  Args:
      aggr (string, optional): The aggregation scheme to use
          (:obj:`"add"`, :obj:`"mean"`, :obj:`"max"` or :obj:`None`).
          (default: :obj:`"add"`)
      flow (string, optional): The flow direction of message passing
          (:obj:`"source_to_target"` or :obj:`"target_to_source"`).
          (default: :obj:`"source_to_target"`)
      node_dim (int, optional): The axis along which to propagate.
          (default: :obj:`-2`)
docstr: Constructs messages from node :math:`j` to node :math:`i`
  in analogy to :math:`\phi_{\mathbf{\Theta}}` for each edge in
  :obj:`edge_index`.
  This function can take any argument as input which was initially
  passed to :meth:`propagate`.
  Furthermore, tensors passed to :meth:`propagate` can be mapped to the
  respective nodes :math:`i` and :math:`j` by appending :obj:`_i` or
  :obj:`_j` to the variable name, *.e.g.* :obj:`x_i` and :obj:`x_j`.
docstr_tok: ["Constructs","messages","from","node",":","math",":","`","j","`","to","node",":","math",":","`","i","`","in","analogy","to",":","math",":","`","\\phi_","{","\\mathbf","{","\\Theta","}","}","`","for","each","edge","in",":","obj",":","`","edge_index","`",".","This","function","can","take","any","argument","as","input","which","was","initially","passed","to",":","meth",":","`","propagate","`",".","Furthermore",",","tensors","passed","to",":","meth",":","`","propagate","`","can","be","mapped","to","the","respective","nodes",":","math",":","`","i","`","and",":","math",":","`","j","`","by","appending",":","obj",":","`","_i","`","or",":","obj",":","`","_j","`","to","the","variable","name",",","*",".e.g",".","*",":","obj",":","`","x_i","`","and",":","obj",":","`","x_j","`","."]
code: def message(self, x_j: Tensor) -> Tensor:
      r"""Constructs messages from node :math:`j` to node :math:`i`
      in analogy to :math:`\phi_{\mathbf{\Theta}}` for each edge in
      :obj:`edge_index`.
      This function can take any argument as input which was initially
      passed to :meth:`propagate`.
      Furthermore, tensors passed to :meth:`propagate` can be mapped to the
      respective nodes :math:`i` and :math:`j` by appending :obj:`_i` or
      :obj:`_j` to the variable name, *.e.g.* :obj:`x_i` and :obj:`x_j`.
      """
      return x_j
  
imports: import os
  import re
  import inspect
  import os.path
  from uuid import uuid1
  from itertools import chain
  from inspect import Parameter
  from typing import List, Optional, Set
  from torch_geometric.typing import Adj, Size
  import torch
  from torch import Tensor
  from jinja2 import Template
  from torch_sparse import SparseTensor
  from torch_scatter import gather_csr, scatter, segment_csr
  from .utils.helpers import expand_left
  from .utils.jit import class_from_module_repr
  from .utils.typing import sanitize, split_types_repr, parse_types, resolve_types
  from .utils.inspector import Inspector, func_header_repr, func_body_repr
label_desc: Use image node_id 7 for calling the MessagePassing obj's underlying member method code with example usage: obj.message(x_j) and returns: x_j