How to use the fairseq.utils.resolve_max_positions function in fairseq

def validate(args, trainer, task, epoch_itr, subsets):
    """Evaluate the model on the validation set(s) and return the losses."""
    valid_losses = []
    for subset in subsets:
        # Initialize data iterator
        itr = task.get_batch_iterator(
            dataset=task.dataset(subset),
            max_tokens=args.max_tokens_valid,
            max_sentences=args.max_sentences_valid,
            max_positions=utils.resolve_max_positions(
                task.max_positions(),
                trainer.get_model().max_positions(),
            ),
            ignore_invalid_inputs=args.skip_invalid_size_inputs_valid_test,
            required_batch_size_multiple=args.required_batch_size_multiple,
            seed=args.seed,
            num_shards=args.distributed_world_size,
            shard_id=args.distributed_rank,
            num_workers=args.num_workers,
        ).next_epoch_itr(shuffle=False)
        progress = progress_bar.build_progress_bar(
            args, itr, epoch_itr.epoch,
            prefix='valid on \'{}\' subset'.format(subset),
            no_progress_bar='simple'
        )

beamable_mm_beam_size=None if args.no_beamable_mm else args.beam,
            need_attn=args.print_alignment,
        )
        if args.fp16:
            model.half()

    # Load alignment dictionary for unknown word replacement
    # (None if no unknown word replacement, empty if no path to align dictionary)
    align_dict = utils.load_align_dict(args.replace_unk)

    # Load dataset (possibly sharded)
    itr = task.get_batch_iterator(
        dataset=task.dataset(args.gen_subset),
        max_tokens=args.max_tokens,
        max_sentences=args.max_sentences,
        max_positions=utils.resolve_max_positions(
            task.max_positions(),
            *[model.max_positions() for model in models]
        ),
        ignore_invalid_inputs=args.skip_invalid_size_inputs_valid_test,
        required_batch_size_multiple=8,
        num_shards=args.num_shards,
        shard_id=args.shard_id,
    ).next_epoch_itr(shuffle=False)

    # Initialize generator
    gen_timer = StopwatchMeter()
    if args.score_reference:
        translator = SequenceScorer(models, task.target_dictionary)
    else:
        translator = SequenceGenerator(
            models, task.target_dictionary, beam_size=args.beam, minlen=args.min_len,

)

    # Optimize ensemble for generation
    for model in models:
        # model.make_generation_fast_()
        if args.fp16:
            model.half()
        if use_cuda:
            model.cuda()

    # Load dataset (possibly sharded)
    itr = task.get_batch_iterator(
        dataset=task.dataset(args.gen_subset),
        max_tokens=args.max_tokens,
        max_sentences=args.max_sentences,
        max_positions=utils.resolve_max_positions(
            task.max_positions(),
            *[model.max_positions() for model in models]
        ),
        ignore_invalid_inputs=args.skip_invalid_size_inputs_valid_test,
        required_batch_size_multiple=args.required_batch_size_multiple,
        num_shards=args.num_shards,
        shard_id=args.shard_id,
        num_workers=args.num_workers,
    ).next_epoch_itr(shuffle=False)

    # Initialize generator
    gen_timer = StopwatchMeter()
    generator = task.build_generator(args)

    # Generate and compute score
    coco = task.dataset(args.gen_subset).coco

models = [fasterrcnn.model]

    # Optimize ensemble for generation
    for model in models:
        # model.make_generation_fast_()
        if args.fp16:
            model.half()
        if use_cuda:
            model.cuda()

    # Load dataset (possibly sharded)
    itr = task.get_batch_iterator(
        dataset=task.dataset(args.valid_subset),
        max_tokens=args.max_tokens,
        max_sentences=args.max_sentences,
        max_positions=utils.resolve_max_positions(
            task.max_positions(),
            *[model.max_positions() for model in models]
        ),
        ignore_invalid_inputs=args.skip_invalid_size_inputs_valid_test,
        required_batch_size_multiple=args.required_batch_size_multiple,
        num_shards=args.distributed_world_size,
        shard_id=args.distributed_rank,
        num_workers=args.num_workers,
    ).next_epoch_itr(shuffle=False)

    # Initialize generator
    gen_timer = StopwatchMeter()
    generator = task.build_generator(args)

    # Generate and compute score
    coco = task.dataset(args.valid_subset).coco

def validate(args, trainer, task, epoch_itr, subsets):
    """Evaluate the model on the validation set(s) and return the losses."""

    if args.fixed_validation_seed is not None:
        # set fixed seed for every validation
        utils.set_torch_seed(args.fixed_validation_seed)

    valid_losses = []
    for subset in subsets:
        # Initialize data iterator
        itr = task.get_batch_iterator(
            dataset=task.dataset(subset),
            max_tokens=args.max_tokens_valid,
            max_sentences=args.max_sentences_valid,
            max_positions=utils.resolve_max_positions(
                task.max_positions(),
                trainer.get_model().max_positions(),
            ),
            ignore_invalid_inputs=args.skip_invalid_size_inputs_valid_test,
            required_batch_size_multiple=args.required_batch_size_multiple,
            seed=args.seed,
            num_shards=args.distributed_world_size,
            shard_id=args.distributed_rank,
            num_workers=args.num_workers,
        ).next_epoch_itr(shuffle=False)
        progress = progress_bar.build_progress_bar(
            args, itr, epoch_itr.epoch,
            prefix='valid on \'{}\' subset'.format(subset),
            no_progress_bar='simple'
        )

def __init__(self, checkpoints, device=None, beam_size=5, use_fp16=False, tuning_ops=None):
        torch.manual_seed(checkpoints.args.seed)

        self._checkpoints = checkpoints
        self._device = device
        self._model = self._create_model(checkpoints, device=device, beam_size=beam_size, use_fp16=use_fp16)
        self._translator = self._create_translator(checkpoints, beam_size)
        self._tuner = self._create_tuner(checkpoints, self._model, tuning_ops, device)
        self._max_positions = fairseq.utils.resolve_max_positions(
            checkpoints.task.max_positions(),
            self._model.max_positions(),
        )

        self._logger = logging.getLogger('Transformer')

        self._nn_needs_reset = True
        self._checkpoint = None

for model in models:
        model.make_generation_fast_()
        if args.fp16:
            model.half()
        if use_cuda:
            model.cuda()

    assert len(models) > 0

    print('num. model params: {}'.format(sum(p.numel() for p in models[0].parameters())))

    itr = task.get_batch_iterator(
        dataset=dataset,
        max_tokens=args.max_tokens or 36000,
        max_sentences=args.max_sentences,
        max_positions=utils.resolve_max_positions(*[
            model.max_positions() for model in models
        ]),
        ignore_invalid_inputs=True,
        num_shards=args.num_shards,
        shard_id=args.shard_id,
        num_workers=args.num_workers,
    ).next_epoch_itr(shuffle=False)

    gen_timer = StopwatchMeter()
    scorer = SequenceScorer(task.target_dictionary, args.softmax_batch)

    score_sum = 0.
    count = 0

    if args.remove_bpe is not None:
        if args.remove_bpe == 'sentencepiece':

def get_train_iterator(self, epoch, combine=True):
        """Return an EpochBatchIterator over the training set for a given epoch."""
        print('| loading train data for epoch {}'.format(epoch))
        self.task.load_dataset(self.args.train_subset, epoch=epoch, combine=combine)
        return self.task.get_batch_iterator(
            dataset=self.task.dataset(self.args.train_subset),
            max_tokens=self.args.max_tokens,
            max_sentences=self.args.max_sentences,
            max_positions=utils.resolve_max_positions(
                self.task.max_positions(),
                self.model.max_positions(),
            ),
            ignore_invalid_inputs=True,
            required_batch_size_multiple=self.args.required_batch_size_multiple,
            seed=self.args.seed,
            num_shards=self.args.distributed_world_size,
            shard_id=self.args.distributed_rank,
            num_workers=self.args.num_workers,
            epoch=epoch,
        )

def validate(args, trainer, task, epoch_itr, subsets):
    """Evaluate the model on the validation set(s) and return the losses."""
    valid_losses = []
    for subset in subsets:
        # Initialize data iterator
        itr = task.get_batch_iterator(
            dataset=task.dataset(subset),
            max_tokens=args.max_tokens_valid,
            max_sentences=args.max_sentences_valid,
            max_positions=utils.resolve_max_positions(
                task.max_positions(),
                trainer.get_model().max_positions(),
            ),
            ignore_invalid_inputs=args.skip_invalid_size_inputs_valid_test,
            required_batch_size_multiple=args.required_batch_size_multiple,
            seed=args.seed,
            num_shards=args.distributed_world_size,
            shard_id=args.distributed_rank,
            num_workers=args.num_workers,
        ).next_epoch_itr(shuffle=False)
        progress = progress_bar.build_progress_bar(
            args, itr, epoch_itr.epoch,
            prefix='valid on \'{}\' subset'.format(subset),
            no_progress_bar='simple'
        )