code: def __init__(
      self,
      parent,
      batch_size=13,
      image_size=32,
      num_channels=3,
      num_stages=4,
      hidden_sizes=[10, 20, 30, 40],
      depths=[2, 2, 3, 2],
      is_training=True,
      use_labels=True,
      intermediate_size=37,
      hidden_act="gelu",
      type_sequence_label_size=10,
      initializer_range=0.02,
      num_labels=3,
      scope=None,
  ):
      self.parent = parent
      self.batch_size = batch_size
      self.image_size = image_size
      self.num_channels = num_channels
      self.num_stages = num_stages
      self.hidden_sizes = hidden_sizes
      self.depths = depths
      self.is_training = is_training
      self.use_labels = use_labels
      self.intermediate_size = intermediate_size
      self.hidden_act = hidden_act
      self.type_sequence_label_size = type_sequence_label_size
      self.initializer_range = initializer_range
      self.scope = scope
  
imports: import inspect
  import unittest
  from typing import List, Tuple
  from transformers import ConvNextConfig
  from transformers.testing_utils import require_tf, require_vision, slow
  from transformers.utils import cached_property, is_tf_available, is_vision_available
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
label_desc: Use image node_id 1 to create a new TFConvNextModelTester object  with example: obj = TFConvNextModelTester(parent, batch_size, image_size, num_channels, num_stages, hidden_sizes, depths, is_training, use_labels, intermediate_size, hidden_act, type_sequence_label_size, initializer_range, num_labels, scope)