class_docstr: GraphSAGE layer from `Inductive Representation Learning on Large Graphs
  <https://arxiv.org/abs/1706.02216>`__
  
  We recommend to use this module when appying GraphSAGE on dense graphs.
  
  Note that we only support gcn aggregator in DenseSAGEConv.
  
  Parameters
  ----------
  in_feats : int
      Input feature size; i.e, the number of dimensions of :math:`h_i^{(l)}`.
  out_feats : int
      Output feature size; i.e, the number of dimensions of :math:`h_i^{(l+1)}`.
  feat_drop : float, optional
      Dropout rate on features. Default: 0.
  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.
  activation : callable activation function/layer or None, optional
      If not None, applies an activation function to the updated node features.
      Default: ``None``.
  
  Example
  -------
  >>> import dgl
  >>> import numpy as np
  >>> import torch as th
  >>> from dgl.nn import DenseSAGEConv
  >>>
  >>> 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 = DenseSAGEConv(10, 2)
  >>> res = conv(adj, feat)
  >>> res
  tensor([[1.0401, 2.1008],
          [1.0401, 2.1008],
          [1.0401, 2.1008],
          [1.0401, 2.1008],
          [1.0401, 2.1008],
          [1.0401, 2.1008]], grad_fn=<AddmmBackward>)
  
  See also
  --------
  `SAGEConv <https://docs.dgl.ai/api/python/nn.pytorch.html#sageconv>`__
docstr: Description
  -----------
  Reinitialize learnable parameters.
  
  Notes
  -----
  The linear weights :math:`W^{(l)}` are initialized using Glorot uniform initialization.
docstr_tok: ["Description","--","--","--","--","--","-","Reinitialize","learnable","parameters",".","Notes","--","--","-","The","linear","weights",":","math",":","`","W^","{","(","l",")","}","`","are","initialized","using","Glorot","uniform","initialization","."]
code: def reset_parameters(self):
      r"""
  
      Description
      -----------
      Reinitialize learnable parameters.
  
      Notes
      -----
      The linear weights :math:`W^{(l)}` are initialized using Glorot uniform initialization.
      """
      gain = nn.init.calculate_gain("relu")
      nn.init.xavier_uniform_(self.fc.weight, gain=gain)
  
imports: from torch import nn
  from ....utils import check_eq_shape
label_desc: Use image node_id 2 for calling the DenseSAGEConv obj's underlying member method code with example usage: obj.reset_parameters() without return types