class_docstr: Topology Adaptive Graph Convolutional layer from `Topology
  Adaptive Graph Convolutional Networks <https://arxiv.org/pdf/1710.10370.pdf>`__
  
  .. math::
      H^{K} = {\sum}_{k=0}^K (D^{-1/2} A D^{-1/2})^{k} X {\Theta}_{k},
  
  where :math:`A` denotes the adjacency matrix,
  :math:`D_{ii} = \sum_{j=0} A_{ij}` its diagonal degree matrix,
  :math:`{\Theta}_{k}` denotes the linear weights to sum the results of different hops together.
  
  Parameters
  ----------
  in_feats : int
      Input feature size. i.e, the number of dimensions of :math:`X`.
  out_feats : int
      Output feature size.  i.e, the number of dimensions of :math:`H^{K}`.
  k: int, optional
      Number of hops :math:`K`. Default: ``2``.
  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``.
  
  Attributes
  ----------
  lin : torch.Module
      The learnable linear module.
  
  Example
  -------
  >>> import dgl
  >>> import numpy as np
  >>> import torch as th
  >>> from dgl.nn import TAGConv
  >>>
  >>> g = dgl.graph(([0,1,2,3,2,5], [1,2,3,4,0,3]))
  >>> feat = th.ones(6, 10)
  >>> conv = TAGConv(10, 2, k=2)
  >>> res = conv(g, feat)
  >>> res
  tensor([[ 0.5490, -1.6373],
          [ 0.5490, -1.6373],
          [ 0.5490, -1.6373],
          [ 0.5513, -1.8208],
          [ 0.5215, -1.6044],
          [ 0.3304, -1.9927]], grad_fn=<AddmmBackward>)
docstr: Description
  -----------
  Reinitialize learnable parameters.
  
  Note
  ----
  The model parameters are initialized using Glorot uniform initialization.
docstr_tok: ["Description","--","--","--","--","--","-","Reinitialize","learnable","parameters",".","Note","--","--","The","model","parameters","are","initialized","using","Glorot","uniform","initialization","."]
code: def reset_parameters(self):
      r"""
  
      Description
      -----------
      Reinitialize learnable parameters.
  
      Note
      ----
      The model parameters are initialized using Glorot uniform initialization.
      """
      gain = nn.init.calculate_gain("relu")
      nn.init.xavier_normal_(self.lin.weight, gain=gain)
  
file_path: dgl/python/dgl/nn/pytorch/conv/tagconv.py
imports: import torch
  from torch import nn
  from ....None import function
  from .graphconv import EdgeWeightNorm
label_desc: Use image node_id 2 for calling the TAGConv obj's underlying member method code with example usage: obj.reset_parameters() without return types