class_docstr: Gated Graph Convolution layer from `Gated Graph Sequence
  Neural Networks <https://arxiv.org/pdf/1511.05493.pdf>`__
  
  .. math::
      h_{i}^{0} &= [ x_i \| \mathbf{0} ]
  
      a_{i}^{t} &= \sum_{j\in\mathcal{N}(i)} W_{e_{ij}} h_{j}^{t}
  
      h_{i}^{t+1} &= \mathrm{GRU}(a_{i}^{t}, h_{i}^{t})
  
  Parameters
  ----------
  in_feats : int
      Input feature size; i.e, the number of dimensions of :math:`x_i`.
  out_feats : int
      Output feature size; i.e., the number of dimensions of :math:`h_i^{(t+1)}`.
  n_steps : int
      Number of recurrent steps; i.e, the :math:`t` in the above formula.
  n_etypes : int
      Number of edge types.
  bias : bool
      If True, adds a learnable bias to the output. Default: ``True``.
      Can only be set to True in MXNet.
  
  Example
  -------
  >>> import dgl
  >>> import numpy as np
  >>> import mxnet as mx
  >>> from dgl.nn import GatedGraphConv
  >>>
  >>> g = dgl.graph(([0,1,2,3,2,5], [1,2,3,4,0,3]))
  >>> feat = mx.nd.ones((6, 10))
  >>> conv = GatedGraphConv(10, 10, 2, 3)
  >>> conv.initialize(ctx=mx.cpu(0))
  >>> etype = mx.nd.array([0,1,2,0,1,2])
  >>> res = conv(g, feat, etype)
  >>> res
  [[0.24378185 0.17402579 0.2644723  0.2740628  0.14041871 0.32523093
  0.2703067  0.18234392 0.32777587 0.30957845]
  [0.17872348 0.28878236 0.2509409  0.20139427 0.3355541  0.22643831
  0.2690711  0.22341749 0.27995753 0.21575949]
  [0.23911178 0.16696918 0.26120248 0.27397877 0.13745922 0.3223175
  0.27561218 0.18071817 0.3251124  0.30608907]
  [0.25242943 0.3098581  0.25249368 0.27968448 0.24624602 0.12270881
  0.335147   0.31550157 0.19065917 0.21087633]
  [0.17503153 0.29523152 0.2474858  0.20848347 0.3526433  0.23443702
  0.24741334 0.21986549 0.28935105 0.21859099]
  [0.2159364  0.26942077 0.23083271 0.28329757 0.24758333 0.24230732
  0.23958017 0.23430146 0.26431587 0.27001363]]
  <NDArray 6x10 @cpu(0)>
code: def __init__(self, in_feats, out_feats, n_steps, n_etypes, bias=True):
      super(GatedGraphConv, self).__init__()
      self._in_feats = in_feats
      self._out_feats = out_feats
      self._n_steps = n_steps
      self._n_etypes = n_etypes
      if not bias:
          raise KeyError(
              "MXNet do not support disabling bias in GRUCell."
          )
      with self.name_scope():
          self.linears = nn.Sequential()
          for _ in range(n_etypes):
              self.linears.add(
                  nn.Dense(
                      out_feats,
                      weight_initializer=mx.init.Xavier(),
                      in_units=out_feats,
                  )
              )
          self.gru = gluon.rnn.GRUCell(out_feats, input_size=out_feats)
  
file_path: dgl/python/dgl/nn/mxnet/conv/gatedgraphconv.py
imports: import mxnet
  from mxnet import gluon, nd
  from mxnet.gluon import nn
  from ....None import function
label_desc: Use image node_id 1 to create a new GatedGraphConv object from inherited base classes: nn with example: obj = GatedGraphConv(in_feats, out_feats, n_steps, n_etypes, bias)