class_docstr: Graph Isomorphism layer from `How Powerful are Graph
  Neural Networks? <https://arxiv.org/pdf/1810.00826.pdf>`__
  
  .. math::
      h_i^{(l+1)} = f_\Theta \left((1 + \epsilon) h_i^{l} +
      \mathrm{aggregate}\left(\left\{h_j^{l}, j\in\mathcal{N}(i)
      \right\}\right)\right)
  
  Parameters
  ----------
  apply_func : callable activation function/layer or None
      If not None, apply this function to the updated node feature,
      the :math:`f_\Theta` in the formula.
  aggregator_type : str
      Aggregator type to use (``sum``, ``max`` or ``mean``).
  init_eps : float, optional
      Initial :math:`\epsilon` value, default: ``0``.
  learn_eps : bool, optional
      If True, :math:`\epsilon` will be a learnable parameter. Default: ``False``.
  
  Example
  -------
  >>> import dgl
  >>> import numpy as np
  >>> import mxnet as mx
  >>> from mxnet import gluon
  >>> from dgl.nn import GINConv
  >>>
  >>> g = dgl.graph(([0,1,2,3,2,5], [1,2,3,4,0,3]))
  >>> feat = mx.nd.ones((6, 10))
  >>> lin = gluon.nn.Dense(10)
  >>> lin.initialize(ctx=mx.cpu(0))
  >>> conv = GINConv(lin, 'max')
  >>> conv.initialize(ctx=mx.cpu(0))
  >>> res = conv(g, feat)
  >>> res
  [[ 0.44832918 -0.05283341  0.20823681  0.16020004  0.37311912 -0.03372726
  -0.05716725 -0.20730163  0.14121324  0.46083626]
  [ 0.44832918 -0.05283341  0.20823681  0.16020004  0.37311912 -0.03372726
  -0.05716725 -0.20730163  0.14121324  0.46083626]
  [ 0.44832918 -0.05283341  0.20823681  0.16020004  0.37311912 -0.03372726
  -0.05716725 -0.20730163  0.14121324  0.46083626]
  [ 0.44832918 -0.05283341  0.20823681  0.16020004  0.37311912 -0.03372726
  -0.05716725 -0.20730163  0.14121324  0.46083626]
  [ 0.44832918 -0.05283341  0.20823681  0.16020004  0.37311912 -0.03372726
  -0.05716725 -0.20730163  0.14121324  0.46083626]
  [ 0.22416459 -0.0264167   0.10411841  0.08010002  0.18655956 -0.01686363
  -0.02858362 -0.10365082  0.07060662  0.23041813]]
  <NDArray 6x10 @cpu(0)>
code: def __init__(
      self, apply_func, aggregator_type, init_eps=0, learn_eps=False
  ):
      super(GINConv, self).__init__()
      if aggregator_type == "sum":
          self._reducer = fn.sum
      elif aggregator_type == "max":
          self._reducer = fn.max
      elif aggregator_type == "mean":
          self._reducer = fn.mean
      else:
          raise KeyError(
              "Aggregator type {} not recognized.".format(
                  aggregator_type
              )
          )
  
      with self.name_scope():
          self.apply_func = apply_func
          self.eps = self.params.get(
              "eps",
              shape=(1,),
              grad_req="write" if learn_eps else "null",
              init=mx.init.Constant(init_eps),
          )
  
file_path: dgl/python/dgl/nn/mxnet/conv/ginconv.py
imports: import mxnet
  from mxnet.gluon import nn
  from ....None import function
  from ....utils import expand_as_pair
label_desc: Use image node_id 1 to create a new GINConv object from inherited base classes: nn with example: obj = GINConv(apply_func, aggregator_type, init_eps, learn_eps)