'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 RuntimeError("ConvNetwork    needs    'stride'    and    must    be    called    in    the    bulk    feature    extractor    component.")
    input_tensor = get_input_tensor_with_stride(fixed_embeddings, linked_embeddings, stride)
    del context_tensor_arrays, attention_tensor
    conv = tf.expand_dims(input_tensor, 1)
    for i in range((len(self._depths) - 1)):
        with tf.variable_scope(('conv%d' % i), reuse=True) as scope:
            if during_training:
                conv.set_shape([None, 1, None, self._depths[i]])
                conv = self._maybe_apply_dropout(conv, stride)
            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 < (len(self._weights) - 1)) or (not self._output_dim)):
                conv = self._nonlinearity(conv, name=scope.name)
    return [tf.reshape(conv, [(-1), self._depths[(-1)]], name='reshape_activations')]
