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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def hybrid_forward(self, F, inputs, mask=None):
 # pylint: disable=arguments-differ # pylint: disable=unused-argument """Transformer Encoder Attention Cell. Parameters inputs : Symbol or NDArray Input sequence. Shape (batch_size, length, C_in) mask : Symbol or NDArray or None Mask for inputs. Shape (batch_size, length, length) Returns ------- encoder_cell_outputs: list Outputs of the encoder cell. Contains: - outputs of the transformer encoder cell. Shape (batch_size, length, C_out) - additional_outputs of all the transformer encoder cell """

        outputs, attention_weights =\
            self.attention_cell(inputs, inputs, inputs, mask)
        outputs = self.proj(outputs)
        if self._dropout:
            outputs = self.dropout_layer(outputs)
        if self._use_residual:
            outputs = outputs + inputs
        outputs = self.layer_norm(outputs)
        outputs = self.ffn(outputs)
        additional_outputs = []
        if self._output_attention:
            additional_outputs.append(attention_weights)
        return outputs, additional_outputs