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)>
docstr: Compute Gated Graph Convolution layer.
  
  Parameters
  ----------
  graph : DGLGraph
      The graph.
  feat : mxnet.NDArray
      The input feature of shape :math:`(N, D_{in})` where :math:`N`
      is the number of nodes of the graph and :math:`D_{in}` is the
      input feature size.
  etypes : torch.LongTensor
      The edge type tensor of shape :math:`(E,)` where :math:`E` is
      the number of edges of the graph.
  
  Returns
  -------
  mxnet.NDArray
      The output feature of shape :math:`(N, D_{out})` where :math:`D_{out}`
      is the output feature size.
docstr_tok: ["Compute","Gated","Graph","Convolution","layer",".","Parameters","--","--","--","--","--","graph",":","DGLGraph","The","graph",".","feat",":","mxnet.NDArray","The","input","feature","of","shape",":","math",":","`","(","N",",","D_","{","in","}",")","`","where",":","math",":","`","N","`","is","the","number","of","nodes","of","the","graph","and",":","math",":","`","D_","{","in","}","`","is","the","input","feature","size",".","etypes",":","torch.LongTensor","The","edge","type","tensor","of","shape",":","math",":","`","(","E",",",")","`","where",":","math",":","`","E","`","is","the","number","of","edges","of","the","graph",".","Returns","--","--","--","-","mxnet.NDArray","The","output","feature","of","shape",":","math",":","`","(","N",",","D_","{","out","}",")","`","where",":","math",":","`","D_","{","out","}","`","is","the","output","feature","size","."]
code: def forward(self, graph, feat, etypes):
      """Compute Gated Graph Convolution layer.
  
      Parameters
      ----------
      graph : DGLGraph
          The graph.
      feat : mxnet.NDArray
          The input feature of shape :math:`(N, D_{in})` where :math:`N`
          is the number of nodes of the graph and :math:`D_{in}` is the
          input feature size.
      etypes : torch.LongTensor
          The edge type tensor of shape :math:`(E,)` where :math:`E` is
          the number of edges of the graph.
  
      Returns
      -------
      mxnet.NDArray
          The output feature of shape :math:`(N, D_{out})` where :math:`D_{out}`
          is the output feature size.
      """
      with graph.local_scope():
          assert graph.is_homogeneous, (
              "not a homogeneous graph; convert it with to_homogeneous "
              "and pass in the edge type as argument"
          )
          zero_pad = nd.zeros(
              (feat.shape[0], self._out_feats - feat.shape[1]),
              ctx=feat.context,
          )
          feat = nd.concat(feat, zero_pad, dim=-1)
  
          for _ in range(self._n_steps):
              graph.ndata["h"] = feat
              for i in range(self._n_etypes):
                  eids = (etypes.asnumpy() == i).nonzero()[0]
                  eids = (
                      nd.from_numpy(eids, zero_copy=True)
                      .as_in_context(feat.context)
                      .astype(graph.idtype)
                  )
                  if len(eids) > 0:
                      graph.apply_edges(
                          lambda edges: {
                              "W_e*h": self.linears[i](edges.src["h"])
                          },
                          eids,
                      )
              graph.update_all(
                  fn.copy_e("W_e*h", "m"), fn.sum("m", "a")
              )
              a = graph.ndata.pop("a")
              feat = self.gru(a, [feat])[0]
          return feat
  
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 2 for calling the GatedGraphConv obj's underlying member method code with example usage: obj.forward(graph, feat, etypes) and returns: feat