def hybrid_forward(self, F, inputs):<EOL>
<EOL>character_embedding = self._char_embedding(inputs.reshape((-<NUM_LIT:1>, self._max_chars_per_token)))<EOL>character_embedding = F.transpose(character_embedding, axes=(<NUM_LIT:1>, <NUM_LIT:0>, <NUM_LIT:2>))<EOL>token_embedding = self._convolutions(character_embedding)<EOL>out_shape_ref = inputs.slice_axis(axis=-<NUM_LIT:1>, begin=<NUM_LIT:0>, end=<NUM_LIT:1>)<EOL>out_shape_ref = out_shape_ref.broadcast_axes(axis=(<NUM_LIT:2>,),<EOL>size=(self._output_size))<EOL>return token_embedding.reshape_like(out_shape_ref)<EOL>
Compute context insensitive token embeddings for ELMo representations.

Parameters
----------
inputs : NDArray
    Shape (batch_size, sequence_length, max_character_per_token)
    of character ids representing the current batch.

Returns
-------
token_embedding : NDArray
    Shape (batch_size, sequence_length, embedding_size) with context
    insensitive token representations.