code: def __init__(self):
      self.iter = 0
      self.losses = [
          {"loss_1": 1, "loss_2": 2},
          {"loss_1": 2, "loss_2": 2},
          {"loss_1": 1, "loss_2": 2},
          {"loss_1": 1, "loss_2": 2},
      ]
      output = PanopticResults(
          semantic_logits=torch.tensor(
              [[0, 1], [0, 1], [1, 0]]
          ).float(),
          offset_logits=None,
          cluster_scores=torch.tensor([0.5, 1]),
          clusters=[torch.tensor([0]), torch.tensor([1, 2])],
          cluster_type=None,
      )
      labels = PanopticLabels(
          center_label=None,
          y=torch.tensor([1, 1, 1]),
          num_instances=torch.tensor([2]),
          instance_labels=torch.tensor([1, 1, 2]),
          instance_mask=None,
          vote_label=None,
      )
      self.outputs = [output]
      self.labels = [labels]
  
imports: import os
  import sys
  import unittest
  import torch
  import numpy
  from torch_geometric.data import Data
  from torch_points3d.models.panoptic.structures import PanopticResults, PanopticLabels
  from torch_points3d.metrics.panoptic_tracker import _Instance, InstanceAPMeter, PanopticTracker
label_desc: Use image node_id 1 to create a new MockModel object  with example: obj = MockModel()