class_docstr: The simple graph convolutional operator from the `"Simplifying Graph
  Convolutional Networks" <https://arxiv.org/abs/1902.07153>`_ paper.
  
  .. math::
      \mathbf{X}^{\prime} = {\left(\mathbf{\hat{D}}^{-1/2} \mathbf{\hat{A}}
      \mathbf{\hat{D}}^{-1/2} \right)}^K \mathbf{X} \mathbf{\Theta},
  
  where :math:`\mathbf{\hat{A}} = \mathbf{A} + \mathbf{I}` denotes the
  adjacency matrix with inserted self-loops and
  :math:`\hat{D}_{ii} = \sum_{j=0} \hat{A}_{ij}` its diagonal degree matrix.
  The adjacency matrix can include other values than :obj:`1` representing
  edge weights via the optional :obj:`edge_weight` tensor.
  
  Args:
      in_channels (int): Size of each input sample, or :obj:`-1` to derive
          the size from the first input(s) to the forward method.
      out_channels (int): Size of each output sample.
      K (int, optional): Number of hops :math:`K`. (default: :obj:`1`)
      cached (bool, optional): If set to :obj:`True`, the layer will cache
          the computation of :math:`{\left(\mathbf{\hat{D}}^{-1/2}
          \mathbf{\hat{A}} \mathbf{\hat{D}}^{-1/2} \right)}^K \mathbf{X}` on
          first execution, and will use the cached version for further
          executions.
          This parameter should only be set to :obj:`True` in transductive
          learning scenarios. (default: :obj:`False`)
      add_self_loops (bool, optional): If set to :obj:`False`, will not add
          self-loops to the input graph. (default: :obj:`True`)
      bias (bool, optional): If set to :obj:`False`, the layer will not learn
          an additive bias. (default: :obj:`True`)
      **kwargs (optional): Additional arguments of
          :class:`torch_geometric.nn.conv.MessagePassing`.
  
  Shapes:
      - **input:**
        node features :math:`(|\mathcal{V}|, F_{in})`,
        edge indices :math:`(2, |\mathcal{E}|)`,
        edge weights :math:`(|\mathcal{E}|)` *(optional)*
      - **output:**
        node features :math:`(|\mathcal{V}|, F_{out})`
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code: def __init__(
      self,
      in_channels: int,
      out_channels: int,
      K: int = 1,
      cached: bool = False,
      add_self_loops: bool = True,
      bias: bool = True,
      **kwargs,
  ):
      kwargs.setdefault("aggr", "add")
      super().__init__(**kwargs)
  
      self.in_channels = in_channels
      self.out_channels = out_channels
      self.K = K
      self.cached = cached
      self.add_self_loops = add_self_loops
  
      self._cached_x = None
  
      self.lin = Linear(in_channels, out_channels, bias=bias)
  
      self.reset_parameters()
  
file_path: pytorch_geometric/torch_geometric/nn/conv/sg_conv.py
imports: from typing import Optional
  from torch import Tensor
  from torch_geometric.nn.conv import MessagePassing
  from torch_geometric.nn.conv.gcn_conv import gcn_norm
  from torch_geometric.nn.dense.linear import Linear
  from torch_geometric.typing import Adj, OptTensor, SparseTensor
  from torch_geometric.utils import spmm
label_desc: Use image node_id 1 to create a new SGConv object from inherited base classes: MessagePassing with example: obj = SGConv(in_channels, out_channels, K, cached, add__loops, bias)