class_docstr: The objects returned by the tracker's `update` function on each iteration.
  
  They represent the objects currently being tracked by the tracker.
  
  Users should not instantiate TrackedObjects manually;
  the Tracker will be in charge of creating them.
  
  Attributes
  ----------
  estimate : np.ndarray
      Where the tracker predicts the point will be in the current frame based on past detections.
      A numpy array with the same shape as the detections being fed to the tracker that produced it.
  id : Optional[int]
      The unique identifier assigned to this object by the tracker. Set to `None` if the object is initializing.
  global_id : Optional[int]
      The globally unique identifier assigned to this object. Set to `None` if the object is initializing
  last_detection : Detection
      The last detection that matched with this tracked object.
      Useful if you are storing embeddings in your detections and want to do metric learning, or for debugging.
  last_distance : Optional[float]
      The distance the tracker had with the last object it matched with.
  age : int
      The age of this object measured in number of frames.
  live_points :
      A boolean mask with shape `(n_points,)`. Points marked as `True` have recently been matched with detections.
      Points marked as `False` haven't and are to be considered stale, and should be ignored.
  
      Functions like [`draw_tracked_objects`][norfair.drawing.draw_tracked_objects] use this property to determine which points not to draw.
  initializing_id : int
      On top of `id`, objects also have an `initializing_id` which is the id they are given internally by the `Tracker`;
      this id is used solely for debugging.
  
      Each new object created by the `Tracker` starts as an uninitialized `TrackedObject`,
      which needs to reach a certain match rate to be converted into a full blown `TrackedObject`.
      `initializing_id` is the id temporarily assigned to `TrackedObject` while they are getting initialized.
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code: def _acquire_ids(self):
      self.id, self.global_id = self._obj_factory.get_ids()
  
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 14 for calling the TrackedObject obj's underlying member method code with example usage: obj._acquire_ids() without return types