class_docstr: Graph Convolutional layer from `Semi-Supervised Classification with Graph
  Convolutional Networks <https://arxiv.org/abs/1609.02907>`__
  
  We recommend user to use this module when applying graph convolution on
  dense graphs.
  
  Parameters
  ----------
  in_feats : int
      Input feature size; i.e, the number of dimensions of :math:`h_j^{(l)}`.
  out_feats : int
      Output feature size; i.e., the number of dimensions of :math:`h_i^{(l+1)}`.
  norm : str, optional
      How to apply the normalizer. If is `'right'`, divide the aggregated messages
      by each node's in-degrees, which is equivalent to averaging the received messages.
      If is `'none'`, no normalization is applied. Default is `'both'`,
      where the :math:`c_{ij}` in the paper is applied.
  bias : bool, optional
      If True, adds a learnable bias to the output. Default: ``True``.
  activation : callable activation function/layer or None, optional
      If not None, applies an activation function to the updated node features.
      Default: ``None``.
  
  Notes
  -----
  Zero in-degree nodes will lead to all-zero output. A common practice
  to avoid this is to add a self-loop for each node in the graph,
  which can be achieved by setting the diagonal of the adjacency matrix to be 1.
  
  Example
  -------
  >>> import dgl
  >>> import numpy as np
  >>> import torch as th
  >>> from dgl.nn import DenseGraphConv
  >>>
  >>> feat = th.ones(6, 10)
  >>> adj = th.tensor([[0., 0., 1., 0., 0., 0.],
  ...         [1., 0., 0., 0., 0., 0.],
  ...         [0., 1., 0., 0., 0., 0.],
  ...         [0., 0., 1., 0., 0., 1.],
  ...         [0., 0., 0., 1., 0., 0.],
  ...         [0., 0., 0., 0., 0., 0.]])
  >>> conv = DenseGraphConv(10, 2)
  >>> res = conv(adj, feat)
  >>> res
  tensor([[0.2159, 1.9027],
          [0.3053, 2.6908],
          [0.3053, 2.6908],
          [0.3685, 3.2481],
          [0.3053, 2.6908],
          [0.0000, 0.0000]], grad_fn=<AddBackward0>)
  
  See also
  --------
  `GraphConv <https://docs.dgl.ai/api/python/nn.pytorch.html#graphconv>`__
docstr: Reinitialize learnable parameters.
docstr_tok: ["Reinitialize","learnable","parameters","."]
code: def reset_parameters(self):
      """Reinitialize learnable parameters."""
      init.xavier_uniform_(self.weight)
      if self.bias is not None:
          init.zeros_(self.bias)
  
file_path: dgl/python/dgl/nn/pytorch/conv/densegraphconv.py
imports: import torch
  from torch import nn
  from torch.nn import init
label_desc: Use image node_id 2 for calling the DenseGraphConv obj's underlying member method code with example usage: obj.reset_parameters() without return types