class_docstr: SGC layer from `Simplifying Graph
  Convolutional Networks <https://arxiv.org/pdf/1902.07153.pdf>`__
  
  .. math::
      H^{K} = (\tilde{D}^{-1/2} \tilde{A} \tilde{D}^{-1/2})^K X \Theta
  
  where :math:`\tilde{A}` is :math:`A` + :math:`I`.
  Thus the graph input is expected to have self-loop edges added.
  
  Parameters
  ----------
  in_feats : int
      Number of input features; i.e, the number of dimensions of :math:`X`.
  out_feats : int
      Number of output features; i.e, the number of dimensions of :math:`H^{K}`.
  k : int
      Number of hops :math:`K`. Defaults:``1``.
  cached : bool
      If True, the module would cache
  
      .. math::
          (\tilde{D}^{-\frac{1}{2}}\tilde{A}\tilde{D}^{-\frac{1}{2}})^K X\Theta
  
      at the first forward call. This parameter should only be set to
      ``True`` in Transductive Learning setting.
  bias : bool
      If True, adds a learnable bias to the output. Default: ``True``.
  norm : callable activation function/layer or None, optional
      If not None, applies normalization to the updated node features.  Default: ``False``.
  allow_zero_in_degree : bool, optional
      If there are 0-in-degree nodes in the graph, output for those nodes will be invalid
      since no message will be passed to those nodes. This is harmful for some applications
      causing silent performance regression. This module will raise a DGLError if it detects
      0-in-degree nodes in input graph. By setting ``True``, it will suppress the check
      and let the users handle it by themselves. Default: ``False``.
  
  Note
  ----
  Zero in-degree nodes will lead to invalid output value. This is because no message
  will be passed to those nodes, the aggregation function will be appied on empty input.
  A common practice to avoid this is to add a self-loop for each node in the graph if
  it is homogeneous, which can be achieved by:
  
  >>> g = ... # a DGLGraph
  >>> g = dgl.add_self_loop(g)
  
  Calling ``add_self_loop`` will not work for some graphs, for example, heterogeneous graph
  since the edge type can not be decided for self_loop edges. Set ``allow_zero_in_degree``
  to ``True`` for those cases to unblock the code and handle zero-in-degree nodes manually.
  A common practise to handle this is to filter out the nodes with zero-in-degree when use
  after conv.
  
  Example
  -------
  >>> import dgl
  >>> import numpy as np
  >>> import torch as th
  >>> from dgl.nn import SGConv
  >>>
  >>> g = dgl.graph(([0,1,2,3,2,5], [1,2,3,4,0,3]))
  >>> g = dgl.add_self_loop(g)
  >>> feat = th.ones(6, 10)
  >>> conv = SGConv(10, 2, k=2)
  >>> res = conv(g, feat)
  >>> res
  tensor([[-1.9441, -0.9343],
          [-1.9441, -0.9343],
          [-1.9441, -0.9343],
          [-2.7709, -1.3316],
          [-1.9297, -0.9273],
          [-1.9441, -0.9343]], grad_fn=<AddmmBackward>)
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docstr: Description
  -----------
  Reinitialize learnable parameters.
  
  Note
  ----
  The model parameters are initialized using xavier initialization
  and the bias is initialized to be zero.
docstr_tok: ["Description","--","--","--","--","--","-","Reinitialize","learnable","parameters",".","Note","--","--","The","model","parameters","are","initialized","using","xavier","initialization","and","the","bias","is","initialized","to","be","zero","."]
code: def reset_parameters(self):
      r"""
  
      Description
      -----------
      Reinitialize learnable parameters.
  
      Note
      ----
      The model parameters are initialized using xavier initialization
      and the bias is initialized to be zero.
      """
      nn.init.xavier_uniform_(self.fc.weight)
      if self.fc.bias is not None:
          nn.init.zeros_(self.fc.bias)
  
file_path: dgl/python/dgl/nn/pytorch/conv/sgconv.py
imports: import torch
  from torch import nn
  from ....None import function
  from ....base import DGLError
  from .graphconv import EdgeWeightNorm
label_desc: Use image node_id 2 for calling the SGConv obj's underlying member method code with example usage: obj.reset_parameters() without return types