code: def __init__(
      self,
      parent,
      batch_size=12,
      image_size=30,
      patch_size=2,
      num_channels=3,
      is_training=True,
      hidden_size=32,
      num_hidden_layers=5,
      num_attention_heads=4,
      intermediate_size=37,
      dropout=0.1,
      attention_dropout=0.1,
      initializer_range=0.02,
      scope=None,
  ):
      self.parent = parent
      self.batch_size = batch_size
      self.image_size = image_size
      self.patch_size = patch_size
      self.num_channels = num_channels
      self.is_training = is_training
      self.hidden_size = hidden_size
      self.num_hidden_layers = num_hidden_layers
      self.num_attention_heads = num_attention_heads
      self.intermediate_size = intermediate_size
      self.dropout = dropout
      self.attention_dropout = attention_dropout
      self.initializer_range = initializer_range
      self.scope = scope
  
imports: import inspect
  import os
  import tempfile
  import unittest
  from importlib import import_module
  import requests
  from transformers import CLIPConfig, CLIPTextConfig, CLIPVisionConfig
  from transformers.testing_utils import require_tf, require_vision, slow
  from transformers.utils import is_tf_available, is_vision_available
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
label_desc: Use image node_id 1 to create a new TFCLIPVisionModelTester object  with example: obj = TFCLIPVisionModelTester(parent, batch_size, image_size, patch_size, num_channels, is_training, hidden_size, num_hidden_layers, num_attention_heads, intermediate_size, dropout, attention_dropout, initializer_range, scope)