class_docstr: Detections returned by the detector must be converted to a `Detection` object before being used by Norfair.
  
  Parameters
  ----------
  points : np.ndarray
      Points detected. Must be a rank 2 array with shape `(n_points, n_dimensions)` where n_dimensions is 2 or 3.
  scores : np.ndarray, optional
      An array of length `n_points` which assigns a score to each of the points defined in `points`.
  
      This is used to inform the tracker of which points to ignore;
      any point with a score below `detection_threshold` will be ignored.
  
      This useful for cases in which detections don't always have every point present, as is often the case in pose estimators.
  data : Any, optional
      The place to store any extra data which may be useful when calculating the distance function.
      Anything stored here will be available to use inside the distance function.
  
      This enables the development of more interesting trackers which can do things like assign an appearance embedding to each
      detection to aid in its tracking.
  label : Hashable, optional
      When working with multiple classes the detection's label can be stored to be used as a matching condition when associating
      tracked objects with new detections. Label's type must be hashable for drawing purposes.
  embedding : Any, optional
      The embedding for the reid_distance.
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code: def update_coordinate_transformation(
      self, coordinate_transformation: CoordinatesTransformation
  ):
      if coordinate_transformation is not None:
          self.absolute_points = coordinate_transformation.rel_to_abs(
              self.absolute_points
          )
  
imports: from logging import warning
  from typing import Any, Callable, Hashable, List, Optional, Sequence, Tuple, Union
  import numpy
  from rich import print
  from norfair.camera_motion import CoordinatesTransformation
  from .distances import AVAILABLE_VECTORIZED_DISTANCES, ScalarDistance, get_distance_by_name
  from .filter import FilterFactory, OptimizedKalmanFilterFactory
  from .utils import validate_points
label_desc: Use image node_id 2 for calling the Detection obj's underlying member method code with example usage: obj.update_coordinate_transformation(coordinate_transformation) without return types