def hybrid_forward(self, F, inputs, mask=None): 
if mask is not None:<EOL><INDENT>inputs = F.broadcast_mul(inputs, mask.expand_dims(-<NUM_LIT:1>))<EOL><DEDENT>inputs = F.transpose(inputs, axes=(<NUM_LIT:1>, <NUM_LIT:2>, <NUM_LIT:0>))<EOL>output = self._convs(inputs)<EOL>if self._highways:<EOL><INDENT>output = self._highways(output)<EOL><DEDENT>if self._projection:<EOL><INDENT>output = self._projection(output)<EOL><DEDENT>return output<EOL>
r"""
        Forward computation for char_encoder

        Parameters
        ----------
        inputs: NDArray
            The input tensor is of shape `(seq_len, batch_size, embedding_size)` TNC.
        mask: NDArray
            The mask applied to the input of shape `(seq_len, batch_size)`, the mask will
            be broadcasted along the embedding dimension.

        Returns
        ----------
        output: NDArray
            The output of the encoder with shape `(batch_size, output_size)`