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: Updates node embeddings in analogy to
  :math:`\gamma_{\mathbf{\Theta}}` for each node
  :math:`i \in \mathcal{V}`.
  Takes in the output of aggregation as first argument and any argument
  which was initially passed to :meth:`propagate`.
docstr_tok: ["Updates","node","embeddings","in","analogy","to",":","math",":","`","\\gamma_","{","\\mathbf","{","\\Theta","}","}","`","for","each","node",":","math",":","`","i","\\in","\\mathcal","{","V","}","`",".","Takes","in","the","output","of","aggregation","as","first","argument","and","any","argument","which","was","initially","passed","to",":","meth",":","`","propagate","`","."]
code: def update(self, inputs: Tensor) -> Tensor:
      r"""Updates node embeddings in analogy to
      :math:`\gamma_{\mathbf{\Theta}}` for each node
      :math:`i \in \mathcal{V}`.
      Takes in the output of aggregation as first argument and any argument
      which was initially passed to :meth:`propagate`.
      """
      return inputs
  
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 10 for calling the MessagePassing obj's underlying member method code with example usage: obj.update(inputs) and returns: inputs