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