Repository: Project-MONAI/MONAI
File: monai/transforms/utils.py (Python)

Code before:
    if isinstance(input_array, monai.data.MetaTensor) and input_array.pending_operations:
        msg = (
            "The input image is a MetaTensor and has pending operations,\n"
            f"but the function {name or ''} assumes non-lazy input, result may be incorrect."
        )
        if raise_error:
            raise ValueError(msg)
        warnings.warn(msg)


def map_and_generate_sampling_centers(
    label: NdarrayOrTensor,
    spatial_size: Sequence[int] | int,
    num_samples: int,
    label_spatial_shape: Sequence[int],
    indices: Sequence[NdarrayOrTensor],
    num_classes: int | None = None,
    image: NdarrayOrTensor | None = None,
    image_threshold: float = 0.0,
    max_samples_per_class: int | None = None,
    ratios: list[float | int] | None = None,
    rand_state: np.random.RandomState | None = None,
    allow_smaller: bool = False,
    warn: bool = True,
) -> tuple[tuple]:
    """
    Combine "map_classes_to_indices" and "generate_label_classes_crop_centers" functions, return crop center coordinates.

    Args:
        label: use the label data to get the indices of every class.
        spatial_size: spatial size of the ROIs to be sampled.
        num_samples: total sample centers to be generated.
        label_spatial_shape: spatial shape of the original label data to unravel selected centers.
        indices: sequence of pre-computed foreground indices of every class in 1 dimension.
        num_classes: number of classes for argmax label, not necessary for One-Hot label.
        image: if image is not None, only return the indices of every class that are within the valid
            region of the image (``image > image_threshold``).
        image_threshold: if enabled `image`, use ``image > image_threshold`` to
            determine the valid image content area and select class indices only in this area.
        max_samples_per_class: maximum length of indices in each class to reduce memory consumption.
            Default is None, no subsampling.
        ratios: ratios of every class in the label to generate crop centers, including background class.
            if None, every class will have the same ratio to generate crop centers.
        rand_state: numpy randomState object to align with other modules.
        allow_smaller: if `False`, an exception will be raised if the image is smaller than
            the requested ROI in any dimension. If `True`, any smaller dimensions will be set to
            match the cropped size (i.e., no cropping in that dimension).
        warn: if `True` prints a warning if a class is not present in the label.

    """
    indices_ = indices if indices is None else indices

Review comment (suggestion):
It might help to write a little more description stating that this will call `map_classes_to_indices` if `indices` is None, pull the shape from the label or image (if label is None), and then call `generate_label_classes_crop_centers`. 

Code after:
    if isinstance(input_array, monai.data.MetaTensor) and input_array.pending_operations:
        msg = (
            "The input image is a MetaTensor and has pending operations,\n"
            f"but the function {name or ''} assumes non-lazy input, result may be incorrect."
        )
        if raise_error:
            raise ValueError(msg)
        warnings.warn(msg)


def map_and_generate_sampling_centers(
    label: NdarrayOrTensor,
    spatial_size: Sequence[int] | int,
    num_samples: int,
    label_spatial_shape: Sequence[int] | None = None,
    num_classes: int | None = None,
    image: NdarrayOrTensor | None = None,
    image_threshold: float = 0.0,
    max_samples_per_class: int | None = None,
    ratios: list[float | int] | None = None,
    rand_state: np.random.RandomState | None = None,
    allow_smaller: bool = False,
    warn: bool = True,
) -> tuple[tuple]:
    """
    Combine "map_classes_to_indices" and "generate_label_classes_crop_centers" functions, return crop center coordinates.
    This calls `map_classes_to_indices` to get indices from `label`, gets the shape from `label_spatial_shape`
    is given otherwise from the labels, calls `generate_label_classes_crop_centers`, and returns its results.

    Args:
        label: use the label data to get the indices of every class.
        spatial_size: spatial size of the ROIs to be sampled.
        num_samples: total sample centers to be generated.
        label_spatial_shape: spatial shape of the original label data to unravel selected centers.
        indices: sequence of pre-computed foreground indices of every class in 1 dimension.
        num_classes: number of classes for argmax label, not necessary for One-Hot label.
        image: if image is not None, only return the indices of every class that are within the valid
            region of the image (``image > image_threshold``).
        image_threshold: if enabled `image`, use ``image > image_threshold`` to
            determine the valid image content area and select class indices only in this area.
        max_samples_per_class: maximum length of indices in each class to reduce memory consumption.
            Default is None, no subsampling.
        ratios: ratios of every class in the label to generate crop centers, including background class.
            if None, every class will have the same ratio to generate crop centers.
        rand_state: numpy randomState object to align with other modules.
        allow_smaller: if `False`, an exception will be raised if the image is smaller than
            the requested ROI in any dimension. If `True`, any smaller dimensions will be set to
            match the cropped size (i.e., no cropping in that dimension).
        warn: if `True` prints a warning if a class is not present in the label.
    Returns:
        Tuple of crop centres