def hybrid_forward(self, F, inputs, mask=None): # pylint: disable=arguments-differ
        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)`

        """
        if mask is not None:
            inputs = F.broadcast_mul(inputs, mask.expand_dims(-1))

        inputs = F.transpose(inputs, axes=(1, 2, 0))

        output = self._convs(inputs)

        if self._highways:
            output = self._highways(output)

        if self._projection:
            output = self._projection(output)

        return output
def hybrid_forward ( self , F , inputs , mask = None ) : if mask is not None : inputs = F . broadcast_mul ( inputs , mask . expand_dims ( - 1 ) ) inputs = F . transpose ( inputs , axes = ( 1 , 2 , 0 ) ) output = self . _convs ( inputs ) if self . _highways : output = self . _highways ( output ) if self . _projection : output = self . _projection ( output ) return output
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)`