def forward(data, model, mlm_loss, nsp_loss, vocab_size, dtype):
(input_id, masked_id, masked_position, masked_weight,next_sentence_label, segment_id, valid_length) = data<EOL>num_masks = masked_weight.sum() + <NUM_LIT><EOL>valid_length = valid_length.reshape(-<NUM_LIT:1>)<EOL>masked_id = masked_id.reshape(-<NUM_LIT:1>)<EOL>valid_length_typed = valid_length.astype(dtype, copy=False)<EOL>_, _, classified, decoded = model(input_id, segment_id, valid_length_typed,<EOL>masked_position)<EOL>decoded = decoded.reshape((-<NUM_LIT:1>, vocab_size))<EOL>ls1 = mlm_loss(decoded.astype('<STR_LIT>', copy=False),<EOL>masked_id, masked_weight.reshape((-<NUM_LIT:1>, <NUM_LIT:1>)))<EOL>ls2 = nsp_loss(classified.astype('<STR_LIT>', copy=False), next_sentence_label)<EOL>ls1 = ls1.sum() / num_masks<EOL>ls2 = ls2.mean()<EOL>ls = ls1 + ls2<EOL>return ls, next_sentence_label, classified, masked_id, decoded,masked_weight, ls1, ls2, valid_length.astype('<STR_LIT>', copy=False)<EOL>
forward computation for evaluation