'Requires |stride|; otherwise see base class.'

def create(self, fixed_embeddings, linked_embeddings, context_tensor_arrays, attention_tensor, during_training, stride=None):

    if (stride is None):
        raise ValueError("PairwiseConvNetwork    needs    'stride'")
    input_tensor = get_input_tensor_with_stride(fixed_embeddings, linked_embeddings, stride)
    del context_tensor_arrays, attention_tensor, during_training
    num_steps = tf.shape(input_tensor)[1]
    arg1 = tf.expand_dims(input_tensor, 1)
    arg1 = tf.tile(arg1, tf.stack([1, num_steps, 1, 1]))
    arg2 = tf.expand_dims(input_tensor, 2)
    arg2 = tf.tile(arg2, tf.stack([1, 1, num_steps, 1]))
    conv = tf.concat([arg1, arg2], 3)
    for i in xrange(self._num_layers):
        with tf.variable_scope(('conv%d' % i), reuse=True) as scope:
            conv = tf.nn.conv2d(conv, self._component.get_variable('weights'), [1, 1, 1, 1], padding='SAME')
            conv = tf.nn.bias_add(conv, self._component.get_variable('biases'))
            if (i in self._relu_layers):
                conv = tf.nn.relu(conv, name=scope.name)
    return [tf.reshape(conv, [(-1), num_steps], name='reshape_activations')]
