'See base class.'

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

    input_tensor = get_input_tensor(fixed_embeddings, linked_embeddings)
    if during_training:
        input_tensor.set_shape([None, self._concatenated_input_dim])
        input_tensor = self._maybe_apply_dropout(input_tensor, stride)
    if self._layer_norm_input:
        input_tensor = self._layer_norm_input.normalize(input_tensor)
    tensors = []
    last_layer = input_tensor
    for (index, hidden_layer_size) in enumerate(self._hidden_layer_sizes):
        acts = tf.matmul(last_layer, self._component.get_variable(('weights_%d' % index)))
        if (during_training and self._attrs['dropout_all_layers'] and (index > 0)):
            acts.set_shape([None, hidden_layer_size])
            acts = self._maybe_apply_dropout(acts, stride)
        if ((index == 0) and self._layer_norm_hidden):
            acts = self._layer_norm_hidden.normalize(acts)
        else:
            acts = tf.nn.bias_add(acts, self._component.get_variable(('bias_%d' % index)))
        last_layer = self._nonlinearity(acts)
        tensors.append(last_layer)
    if self._hidden_layer_sizes:
        tensors.append(last_layer)
    if (self._layers[(-1)].name == 'logits'):
        logits = (tf.matmul(last_layer, self._component.get_variable('weights_softmax')) + self._component.get_variable('bias_softmax'))
        if self._component.spec.attention_component:
            logits += self.attention(last_layer, attention_tensor)
        logits = tf.identity(logits, name=self._layers[(-1)].name)
        tensors.append(logits)
    return tensors
