code: def __init__(
      self,
      parent,
      batch_size=12,
      seq_length=7,
      is_training=True,
      use_input_mask=True,
      use_labels=True,
      vocab_size=99,
      hidden_size=32,
      num_hidden_layers=5,
      num_attention_heads=4,
      intermediate_size=37,
      dropout=0.1,
      attention_dropout=0.1,
      max_position_embeddings=512,
      initializer_range=0.02,
      scope=None,
  ):
      self.parent = parent
      self.batch_size = batch_size
      self.seq_length = seq_length
      self.is_training = is_training
      self.use_input_mask = use_input_mask
      self.use_labels = use_labels
      self.vocab_size = vocab_size
      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.max_position_embeddings = max_position_embeddings
      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 TFCLIPTextModelTester object  with example: obj = TFCLIPTextModelTester(parent, batch_size, seq_length, is_training, use_input_mask, use_labels, vocab_size, hidden_size, num_hidden_layers, num_attention_heads, intermediate_size, dropout, attention_dropout, max_position_embeddings, initializer_range, scope)