code: def prepare_config_and_inputs(self):
      input_ids = ids_tensor(
          [self.batch_size, self.seq_length], self.vocab_size
      )
  
      input_mask = None
      if self.use_input_mask:
          input_mask = random_attention_mask(
              [self.batch_size, self.seq_length]
          )
          # make sure the first token has attention mask `1` to ensure that, after combining the causal mask, there
          # is still at least one token being attended to for each batch.
          # TODO: Change `random_attention_mask` in PT/TF/Flax common test file, after a discussion with the team.
          input_mask = tf.concat(
              [
                  tf.ones_like(
                      input_mask[:, :1], dtype=input_mask.dtype
                  ),
                  input_mask[:, 1:],
              ],
              axis=-1,
          )
  
      config = self.get_config()
  
      return config, input_ids, input_mask
  
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 2 for calling the TFCLIPTextModelTester obj's underlying member method code with example usage: obj.prepare_config_and_inputs() and returns: config,  input_ids,  input_mask