class_docstr: Graph Isomorphism Network 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 tensorflow as tf
  >>> from dgl.nn import GINConv
  >>>
  >>> with tf.device("CPU:0"):
  >>>     g = dgl.graph(([0,1,2,3,2,5], [1,2,3,4,0,3]))
  >>>     feat = tf.ones((6, 10))
  >>>     lin = tf.keras.layers.Dense(10)
  >>>     conv = GINConv(lin, 'max')
  >>>     res = conv(g, feat)
  >>>     res
  <tf.Tensor: shape=(6, 10), dtype=float32, numpy=
  array([[-0.1090256 ,  1.9050574 , -0.30704725, -1.995831  , -0.36399186,
          1.10414   ,  2.4885745 , -0.35387516,  1.3568261 ,  1.7267858 ],
      [-0.1090256 ,  1.9050574 , -0.30704725, -1.995831  , -0.36399186,
          1.10414   ,  2.4885745 , -0.35387516,  1.3568261 ,  1.7267858 ],
      [-0.1090256 ,  1.9050574 , -0.30704725, -1.995831  , -0.36399186,
          1.10414   ,  2.4885745 , -0.35387516,  1.3568261 ,  1.7267858 ],
      [-0.1090256 ,  1.9050574 , -0.30704725, -1.995831  , -0.36399186,
          1.10414   ,  2.4885745 , -0.35387516,  1.3568261 ,  1.7267858 ],
      [-0.1090256 ,  1.9050574 , -0.30704725, -1.995831  , -0.36399186,
          1.10414   ,  2.4885745 , -0.35387516,  1.3568261 ,  1.7267858 ],
      [-0.0545128 ,  0.9525287 , -0.15352362, -0.9979155 , -0.18199593,
          0.55207   ,  1.2442873 , -0.17693758,  0.67841303,  0.8633929 ]],
      dtype=float32)>
code: def __init__(
      self, apply_func, aggregator_type, init_eps=0, learn_eps=False
  ):
      super(GINConv, self).__init__()
      self.apply_func = apply_func
      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
              )
          )
      # to specify whether eps is trainable or not.
      self.eps = tf.Variable(
          initial_value=[init_eps],
          dtype=tf.float32,
          trainable=learn_eps,
      )
  
file_path: dgl/python/dgl/nn/tensorflow/conv/ginconv.py
imports: import tensorflow
  from tensorflow.keras import layers
  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: layers with example: obj = GINConv(apply_func, aggregator_type, init_eps, learn_eps)