code: def prepare_config_and_inputs(self):
      input_ids = ids_tensor(
          [self.batch_size, self.seq_length], self.vocab_size
      )
      input_mask = random_attention_mask(
          [self.batch_size, self.seq_length], dtype=tf.float32
      )
  
      input_lengths = None
      if self.use_input_lengths:
          input_lengths = (
              ids_tensor([self.batch_size], vocab_size=2)
              + self.seq_length
              - 2
          )  # small variation of seq_length
  
      token_type_ids = None
      if self.use_token_type_ids:
          token_type_ids = ids_tensor(
              [self.batch_size, self.seq_length], self.n_langs
          )
  
      sequence_labels = None
      token_labels = None
      is_impossible_labels = None
      if self.use_labels:
          sequence_labels = ids_tensor(
              [self.batch_size], self.type_sequence_label_size
          )
          token_labels = ids_tensor(
              [self.batch_size, self.seq_length], self.num_labels
          )
          is_impossible_labels = ids_tensor(
              [self.batch_size], 2, dtype=tf.float32
          )
          choice_labels = ids_tensor(
              [self.batch_size], self.num_choices
          )
  
      config = FlaubertConfig(
          vocab_size=self.vocab_size,
          n_special=self.n_special,
          emb_dim=self.hidden_size,
          n_layers=self.num_hidden_layers,
          n_heads=self.num_attention_heads,
          dropout=self.hidden_dropout_prob,
          attention_dropout=self.attention_probs_dropout_prob,
          gelu_activation=self.gelu_activation,
          sinusoidal_embeddings=self.sinusoidal_embeddings,
          asm=self.asm,
          causal=self.causal,
          n_langs=self.n_langs,
          max_position_embeddings=self.max_position_embeddings,
          initializer_range=self.initializer_range,
          summary_type=self.summary_type,
          use_proj=self.use_proj,
          bos_token_id=self.bos_token_id,
      )
  
      return (
          config,
          input_ids,
          token_type_ids,
          input_lengths,
          sequence_labels,
          token_labels,
          is_impossible_labels,
          choice_labels,
          input_mask,
      )
  
imports: import unittest
  from transformers import is_tf_available
  from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
label_desc: Use image node_id 2 for calling the TFFlaubertModelTester obj's underlying member method code with example usage: obj.prepare_config_and_inputs() and returns: config,  input_ids,  token_type_ids,  input_lengths,  sequence_labels,  token_labels,  is_impossible_labels,  choice_labels,  input_mask