class_docstr: Attention-based Graph Neural Network layer from `Attention-based Graph Neural Network for
  Semi-Supervised Learning <https://arxiv.org/abs/1803.03735>`__
  
  .. math::
      H^{l+1} = P H^{l}
  
  where :math:`P` is computed as:
  
  .. math::
      P_{ij} = \mathrm{softmax}_i ( \beta \cdot \cos(h_i^l, h_j^l))
  
  where :math:`\beta` is a single scalar parameter.
  
  Parameters
  ----------
  init_beta : float, optional
      The :math:`\beta` in the formula, a single scalar parameter.
  learn_beta : bool, optional
      If True, :math:`\beta` will be learnable parameter.
  allow_zero_in_degree : bool, optional
      If there are 0-in-degree nodes in the graph, output for those nodes will be invalid
      since no message will be passed to those nodes. This is harmful for some applications
      causing silent performance regression. This module will raise a DGLError if it detects
      0-in-degree nodes in input graph. By setting ``True``, it will suppress the check
      and let the users handle it by themselves. Default: ``False``.
  
  Note
  ----
  Zero in-degree nodes will lead to invalid output value. This is because no message
  will be passed to those nodes, the aggregation function will be appied on empty input.
  A common practice to avoid this is to add a self-loop for each node in the graph if
  it is homogeneous, which can be achieved by:
  
  >>> g = ... # a DGLGraph
  >>> g = dgl.add_self_loop(g)
  
  Calling ``add_self_loop`` will not work for some graphs, for example, heterogeneous graph
  since the edge type can not be decided for self_loop edges. Set ``allow_zero_in_degree``
  to ``True`` for those cases to unblock the code and handle zero-in-degree nodes manually.
  A common practise to handle this is to filter out the nodes with zero-in-degree when use
  after conv.
  
  Example
  -------
  >>> import dgl
  >>> import numpy as np
  >>> import torch as th
  >>> from dgl.nn import AGNNConv
  >>>
  >>> g = dgl.graph(([0,1,2,3,2,5], [1,2,3,4,0,3]))
  >>> g = dgl.add_self_loop(g)
  >>> feat = th.ones(6, 10)
  >>> conv = AGNNConv()
  >>> res = conv(g, feat)
  >>> res
  tensor([[1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],
          [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],
          [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],
          [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],
          [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],
          [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]],
      grad_fn=<BinaryReduceBackward>)
docstr: Description
  -----------
  Compute AGNN layer.
  
  Parameters
  ----------
  graph : DGLGraph
      The graph.
  feat : torch.Tensor
      The input feature of shape :math:`(N, *)` :math:`N` is the
      number of nodes, and :math:`*` could be of any shape.
      If a pair of torch.Tensor is given, the pair must contain two tensors of shape
      :math:`(N_{in}, *)` and :math:`(N_{out}, *)`, the :math:`*` in the later
      tensor must equal the previous one.
  
  Returns
  -------
  torch.Tensor
      The output feature of shape :math:`(N, *)` where :math:`*`
      should be the same as input shape.
  
  Raises
  ------
  DGLError
      If there are 0-in-degree nodes in the input graph, it will raise DGLError
      since no message will be passed to those nodes. This will cause invalid output.
      The error can be ignored by setting ``allow_zero_in_degree`` parameter to ``True``.
code: def forward(self, graph, feat):
      r"""
  
      Description
      -----------
      Compute AGNN layer.
  
      Parameters
      ----------
      graph : DGLGraph
          The graph.
      feat : torch.Tensor
          The input feature of shape :math:`(N, *)` :math:`N` is the
          number of nodes, and :math:`*` could be of any shape.
          If a pair of torch.Tensor is given, the pair must contain two tensors of shape
          :math:`(N_{in}, *)` and :math:`(N_{out}, *)`, the :math:`*` in the later
          tensor must equal the previous one.
  
      Returns
      -------
      torch.Tensor
          The output feature of shape :math:`(N, *)` where :math:`*`
          should be the same as input shape.
  
      Raises
      ------
      DGLError
          If there are 0-in-degree nodes in the input graph, it will raise DGLError
          since no message will be passed to those nodes. This will cause invalid output.
          The error can be ignored by setting ``allow_zero_in_degree`` parameter to ``True``.
      """
      with graph.local_scope():
          if not self._allow_zero_in_degree:
              if (graph.in_degrees() == 0).any():
                  raise DGLError(
                      "There are 0-in-degree nodes in the graph, "
                      "output for those nodes will be invalid. "
                      "This is harmful for some applications, "
                      "causing silent performance regression. "
                      "Adding self-loop on the input graph by "
                      "calling `g = dgl.add_self_loop(g)` will resolve "
                      "the issue. Setting ``allow_zero_in_degree`` "
                      "to be `True` when constructing this module will "
                      "suppress the check and let the code run."
                  )
  
          feat_src, feat_dst = expand_as_pair(feat, graph)
  
          graph.srcdata["h"] = feat_src
          graph.srcdata["norm_h"] = F.normalize(feat_src, p=2, dim=-1)
          if isinstance(feat, tuple) or graph.is_block:
              graph.dstdata["norm_h"] = F.normalize(
                  feat_dst, p=2, dim=-1
              )
          # compute cosine distance
          graph.apply_edges(fn.u_dot_v("norm_h", "norm_h", "cos"))
          cos = graph.edata.pop("cos")
          e = self.beta * cos
          graph.edata["p"] = edge_softmax(graph, e)
          graph.update_all(fn.u_mul_e("h", "p", "m"), fn.sum("m", "h"))
          return graph.dstdata.pop("h")
  
file_path: dgl/python/dgl/nn/pytorch/conv/agnnconv.py
imports: import torch
  from torch import nn
  from torch.nn import functional
  from ....None import function
  from ....base import DGLError
  from ....utils import expand_as_pair
  from ...functional import edge_softmax
label_desc: Use image node_id 3 for calling the AGNNConv obj's underlying member method code with example usage: obj.forward(graph, feat) and returns: graph