code: def __init__(
      self,
      parent,
      batch_size=3,
      image_size=32,
      num_channels=3,
      embeddings_size=10,
      hidden_sizes=[10, 20, 30, 40],
      depths=[1, 1, 2, 1],
      is_training=True,
      use_labels=True,
      hidden_act="relu",
      num_labels=3,
      scope=None,
  ):
      self.parent = parent
      self.batch_size = batch_size
      self.image_size = image_size
      self.num_channels = num_channels
      self.embeddings_size = embeddings_size
      self.hidden_sizes = hidden_sizes
      self.depths = depths
      self.is_training = is_training
      self.use_labels = use_labels
      self.hidden_act = hidden_act
      self.num_labels = num_labels
      self.scope = scope
      self.num_stages = len(hidden_sizes)
  
imports: import unittest
  from transformers import RegNetConfig
  from transformers.file_utils import cached_property, is_torch_available, is_vision_available
  from transformers.testing_utils import require_torch, require_vision, slow, torch_device
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
  from ...test_pipeline_mixin import PipelineTesterMixin
label_desc: Use image node_id 1 to create a new RegNetModelTester object  with example: obj = RegNetModelTester(parent, batch_size, image_size, num_channels, embeddings_size, hidden_sizes, depths, is_training, use_labels, hidden_act, num_labels, scope)