SequentialLabels

def forward(self, data: Any) -> Any:
    """Apply the transform.

    The output type always matches the input type.

    Args:
        data: Input data to transform.
    """
    if self.copy:
        data = _copy.deepcopy(data)
    batch, unwrap = self._wrap(data)
    # When per-element gating is active, the transform handles the
    # probability itself (masked-out elements get identity params),
    # so skip the batch-wide coin flip here. Apply iff rand < p, so
    # p=0 is always a no-op and p=1 always applies.
    if not self._per_instance_p_active(batch) and torch.rand(1).item() >= self.p:
        return unwrap(batch)
    params = self.make_params(batch)
    batch = self.apply_transform(batch, params)
    # Record history on the batch, unless every element was gated out by
    # per-element probability: that is an exact no-op, and recording it
    # would let history replay (e.g. an invertible spatial transform)
    # trigger an unnecessary identity resample.
    if not _all_elements_gated_out(params):
        trace = AppliedTransform(name=type(self).__name__, params=params)
        if not hasattr(batch, "applied_transforms"):
            batch.applied_transforms = []
        batch.applied_transforms.append(trace)
    result = unwrap(batch)
    # Propagate history to outputs that can carry it
    if (
        hasattr(batch, "applied_transforms")
        and not isinstance(result, (SubjectsBatch, Tensor, np.ndarray))
        and not isinstance(result, dict)
    ):
        with contextlib.suppress(AttributeError):
            result.applied_transforms = list(batch.applied_transforms)
    return result

def to_hydra(self) -> dict[str, Any]:
    """Export as a Hydra-compatible config dict.

    Returns a dict with `_target_` set to the fully qualified
    class name and only non-default field values included.

    Returns:
        Dict suitable for `hydra.utils.instantiate()`.
    """
    from .parameter_range import _ParameterRange

    cls = type(self)
    target = f"torchio.{cls.__qualname__}"
    cfg: dict[str, Any] = {"_target_": target}

    for name, default in _collect_init_params(cls).items():
        value = getattr(self, name, default)
        if isinstance(value, _ParameterRange):
            if value._original == default:
                continue
            value = _hydra_value(value._original)
        elif value == default:
            continue
        else:
            value = _hydra_value(value)
        cfg[name] = value
    return cfg

def make_params(self, batch: SubjectsBatch) -> dict[str, Any]:
    """Compute the remapping from the first sample's labels."""
    remappings: dict[str, dict[int, int]] = {}
    for name, img_batch in batch.images.items():
        if not issubclass(img_batch._image_class, LabelMap):
            continue
        unique = sorted(int(v) for v in img_batch.data[0].unique().tolist())
        remappings[name] = {old: new for new, old in enumerate(unique)}
    return {"remappings": remappings}

def apply_transform(
    self,
    batch: SubjectsBatch,
    params: dict[str, Any],
) -> SubjectsBatch:
    """Apply sequential renumbering."""
    remappings = params["remappings"]
    for name, img_batch in batch.images.items():
        if name not in remappings:
            continue
        remapping = remappings[name]
        data = torch.zeros_like(img_batch.data)
        for old, new in remapping.items():
            data[img_batch.data == old] = new
        img_batch.data = data
    return batch

def inverse(self, params: dict[str, Any]) -> _SequentialLabelsInverse:
    """Invert by restoring original label values."""
    return _SequentialLabelsInverse(
        remappings=params["remappings"],
        copy=False,
    )