# Copyright (C) 2017-2020 Trent Houliston <trent@houliston.me>
#
# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to
# permit persons to whom the Software is furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
# Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
# COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR
# OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

import tensorflow as tf


class GraphConvolution(tf.keras.layers.Layer):
    def __init__(self, **kwargs):
        super(GraphConvolution, self).__init__()
        self.dense = tf.keras.layers.Dense(**kwargs)

    def call(self, X, G):
        # Call the dense layer with the gathered data
        return self.dense(tf.reshape(tf.gather(X, G, name="NetworkGather"), shape=[-1, X.shape[-1] * G.shape[-1]]))

aa4793a939d4f36a35d7301aa4e012a3f1f984bb
<mask token>


class GraphConvolution(tf.keras.layers.Layer):
    <mask token>
    <mask token>

<mask token>


class GraphConvolution(tf.keras.layers.Layer):

    def __init__(self, **kwargs):
        super(GraphConvolution, self).__init__()
        self.dense = tf.keras.layers.Dense(**kwargs)
    <mask token>

<mask token>


class GraphConvolution(tf.keras.layers.Layer):

    def __init__(self, **kwargs):
        super(GraphConvolution, self).__init__()
        self.dense = tf.keras.layers.Dense(**kwargs)

    def call(self, X, G):
        return self.dense(tf.reshape(tf.gather(X, G, name='NetworkGather'),
            shape=[-1, X.shape[-1] * G.shape[-1]]))

import tensorflow as tf


class GraphConvolution(tf.keras.layers.Layer):

    def __init__(self, **kwargs):
        super(GraphConvolution, self).__init__()
        self.dense = tf.keras.layers.Dense(**kwargs)

    def call(self, X, G):
        return self.dense(tf.reshape(tf.gather(X, G, name='NetworkGather'),
            shape=[-1, X.shape[-1] * G.shape[-1]]))

# Copyright (C) 2017-2020 Trent Houliston <trent@houliston.me>
#
# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to
# permit persons to whom the Software is furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
# Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
# COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR
# OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

import tensorflow as tf


class GraphConvolution(tf.keras.layers.Layer):
    def __init__(self, **kwargs):
        super(GraphConvolution, self).__init__()
        self.dense = tf.keras.layers.Dense(**kwargs)

    def call(self, X, G):
        # Call the dense layer with the gathered data
        return self.dense(tf.reshape(tf.gather(X, G, name="NetworkGather"), shape=[-1, X.shape[-1] * G.shape[-1]]))
