def evaluate(data_loader):
translation_out = []<EOL>all_inst_ids = []<EOL>avg_loss_denom = <NUM_LIT:0><EOL>avg_loss = <NUM_LIT:0.0><EOL>for _, (src_seq, tgt_seq, src_valid_length, tgt_valid_length, inst_ids)in enumerate(data_loader):<EOL><INDENT>src_seq = src_seq.as_in_context(ctx)<EOL>tgt_seq = tgt_seq.as_in_context(ctx)<EOL>src_valid_length = src_valid_length.as_in_context(ctx)<EOL>tgt_valid_length = tgt_valid_length.as_in_context(ctx)<EOL>out, _ = model(src_seq, tgt_seq[:, :-<NUM_LIT:1>], src_valid_length, tgt_valid_length - <NUM_LIT:1>)<EOL>loss = loss_function(out, tgt_seq[:, <NUM_LIT:1>:], tgt_valid_length - <NUM_LIT:1>).mean().asscalar()<EOL>all_inst_ids.extend(inst_ids.asnumpy().astype(np.int32).tolist())<EOL>avg_loss += loss * (tgt_seq.shape[<NUM_LIT:1>] - <NUM_LIT:1>)<EOL>avg_loss_denom += (tgt_seq.shape[<NUM_LIT:1>] - <NUM_LIT:1>)<EOL>samples, _, sample_valid_length =translator.translate(src_seq=src_seq, src_valid_length=src_valid_length)<EOL>max_score_sample = samples[:, <NUM_LIT:0>, :].asnumpy()<EOL>sample_valid_length = sample_valid_length[:, <NUM_LIT:0>].asnumpy()<EOL>for i in range(max_score_sample.shape[<NUM_LIT:0>]):<EOL><INDENT>translation_out.append(<EOL>[tgt_vocab.idx_to_token[ele] for ele in<EOL>max_score_sample[i][<NUM_LIT:1>:(sample_valid_length[i] - <NUM_LIT:1>)]])<EOL><DEDENT><DEDENT>avg_loss = avg_loss / avg_loss_denom<EOL>real_translation_out = [None for _ in range(len(all_inst_ids))]<EOL>for ind, sentence in zip(all_inst_ids, translation_out):<EOL><INDENT>real_translation_out[ind] = sentence<EOL><DEDENT>return avg_loss, real_translation_out<EOL>
Evaluate given the data loader

    Parameters
    ----------
    data_loader : DataLoader

    Returns
    -------
    avg_loss : float
        Average loss
    real_translation_out : list of list of str
        The translation output