class_docstr: The Scalable Inception Graph Neural Network module (SIGN) from the
  `"SIGN: Scalable Inception Graph Neural Networks"
  <https://arxiv.org/abs/2004.11198>`_ paper (functional name: :obj:`sign`),
  which precomputes the fixed representations.
  
  .. math::
      \mathbf{X}^{(i)} = {\left( \mathbf{D}^{-1/2} \mathbf{A}
      \mathbf{D}^{-1/2} \right)}^i \mathbf{X}
  
  for :math:`i \in \{ 1, \ldots, K \}` and saves them in
  :obj:`data.x1`, :obj:`data.x2`, ...
  
  .. note::
  
      Since intermediate node representations are pre-computed, this operator
      is able to scale well to large graphs via classic mini-batching.
      For an example of using SIGN, see `examples/sign.py
      <https://github.com/pyg-team/pytorch_geometric/blob/master/examples/
      sign.py>`_.
  
  Args:
      K (int): The number of hops/layer.
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code: def __repr__(self) -> str:
      return f"{self.__class__.__name__}(K={self.K})"
  
file_path: pytorch_geometric/torch_geometric/transforms/sign.py
imports: import torch
  from torch_geometric import EdgeIndex
  from torch_geometric.data import Data
  from torch_geometric.data.datapipes import functional_transform
  from torch_geometric.transforms import BaseTransform
  from torch_geometric.utils import scatter
label_desc: Use image node_id 3 for calling the SIGN obj's underlying member method code with example usage: obj.__repr__() and returns: self, str, self, str