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``.
  
  See also
  --------
  `SAGEConv <https://docs.dgl.ai/api/python/nn.pytorch.html#sageconv>`__
code: def __init__(
      self,
      in_feats,
      out_feats,
      feat_drop=0.0,
      bias=True,
      norm=None,
      activation=None,
  ):
      super(DenseSAGEConv, self).__init__()
      self._in_feats = in_feats
      self._out_feats = out_feats
      self._norm = norm
      with self.name_scope():
          self.feat_drop = nn.Dropout(feat_drop)
          self.activation = activation
          self.fc = nn.Dense(
              out_feats,
              in_units=in_feats,
              use_bias=bias,
              weight_initializer=mx.init.Xavier(
                  magnitude=math.sqrt(2.0)
              ),
          )
  
file_path: dgl/python/dgl/nn/mxnet/conv/densesageconv.py
imports: import math
  import mxnet
  from mxnet import nd
  from mxnet.gluon import nn
  from ....utils import check_eq_shape
label_desc: Use image node_id 1 to create a new DenseSAGEConv object from inherited base classes: nn with example: obj = DenseSAGEConv(in_feats, out_feats, feat_drop, bias, norm, activation)