code: def centers_in_boxes(
      centers: NdarrayOrTensor,
      boxes: NdarrayOrTensor,
      eps: float = 0.01,
  ) -> NdarrayOrTensor:
      """
      Checks which center points are within boxes
  
      Args:
          boxes: bounding boxes, Nx4 or Nx6 torch tensor or ndarray. The box mode is assumed to be ``StandardMode``.
          centers: center points, Nx2 or Nx3 torch tensor or ndarray.
          eps: minimum distance to border of boxes.
  
      Returns:
          boolean array indicating which center points are within the boxes, sized (N,).
  
      Reference:
          https://github.com/MIC-DKFZ/nnDetection/blob/main/nndet/core/boxes/ops.py
  
      """
      spatial_dims = get_spatial_dims(boxes=boxes)
  
      # compute relative position of centers compared to borders
      # should be non-negative if centers are within boxes
      center_to_border = [
          centers[:, axis] - boxes[:, axis]
          for axis in range(spatial_dims)
      ] + [
          boxes[:, axis + spatial_dims] - centers[:, axis]
          for axis in range(spatial_dims)
      ]
  
      if isinstance(boxes, np.ndarray):
          min_center_to_border: np.ndarray = np.stack(
              center_to_border, axis=1
          ).min(axis=1)
          return min_center_to_border > eps  # array[bool]
  
      return (
          torch.stack(center_to_border, dim=1)
          .to(COMPUTE_DTYPE)
          .min(dim=1)[0]
          > eps
      )
  
imports: from __future__ import annotations
  import inspect
  import warnings
  from abc import ABC, abstractmethod
  from collections.abc import Callable, Sequence
  from copy import deepcopy
  import numpy
  import torch
  from monai.config.type_definitions import NdarrayOrTensor, NdarrayTensor
  from monai.utils import look_up_option
  from monai.utils.enums import BoxModeName
  from monai.utils.type_conversion import convert_data_type, convert_to_dst_type
label_desc: Use image node_id 7 for calling a global function with example usage: centers_in_boxes(centers, boxes, eps) and returns: unknown, unknown