code: def __init__(
      self,
      parent,
      batch_size=2,
      is_training=True,
      use_auxiliary_loss=False,
      num_queries=10,
      num_channels=3,
      min_size=32 * 4,
      max_size=32 * 6,
      num_labels=4,
      mask_feature_size=32,
      num_hidden_layers=2,
      num_attention_heads=2,
  ):
      self.parent = parent
      self.batch_size = batch_size
      self.is_training = is_training
      self.use_auxiliary_loss = use_auxiliary_loss
      self.num_queries = num_queries
      self.num_channels = num_channels
      self.min_size = min_size
      self.max_size = max_size
      self.num_labels = num_labels
      self.mask_feature_size = mask_feature_size
      # This is passed to the decoder config. We add it to the model tester here for testing
      self.num_hidden_layers = num_hidden_layers
      self.num_attention_heads = num_attention_heads
  
imports: import copy
  import unittest
  import numpy
  from tests.test_modeling_common import floats_tensor
  from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
  from transformers.testing_utils import require_torch, require_torch_accelerator, require_torch_fp16, require_torch_multi_gpu, require_vision, slow, torch_device
  from transformers.utils import cached_property
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin
  from ...test_pipeline_mixin import PipelineTesterMixin
label_desc: Use image node_id 1 to create a new MaskFormerModelTester object  with example: obj = MaskFormerModelTester(parent, batch_size, is_training, use_auxiliary_loss, num_queries, num_channels, min_size, max_size, num_labels, mask_feature_size, num_hidden_layers, num_attention_heads)