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`)
code: def __lift__(self, src, edge_index, dim):
      if isinstance(edge_index, Tensor):
          index = edge_index[dim]
          return src.index_select(self.node_dim, index)
      elif isinstance(edge_index, SparseTensor):
          if dim == 1:
              rowptr = edge_index.storage.rowptr()
              rowptr = expand_left(
                  rowptr, dim=self.node_dim, dims=src.dim()
              )
              return gather_csr(src, rowptr)
          elif dim == 0:
              col = edge_index.storage.col()
              return src.index_select(self.node_dim, col)
      raise ValueError
  
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 4 for calling the MessagePassing obj's underlying member method code with example usage: obj.__lift__(src, edge_index, dim) and returns: src, gather_csr, src