code: def _prepare_for_class(
      self, inputs_dict, model_class, return_labels=False
  ):
      inputs_dict = super()._prepare_for_class(
          inputs_dict, model_class, return_labels=return_labels
      )
  
      if return_labels:
          if model_class in get_values(
              TF_MODEL_FOR_PRETRAINING_MAPPING
          ):
              inputs_dict["sentence_order_label"] = tf.zeros(
                  self.model_tester.batch_size, dtype=tf.int32
              )
  
      return inputs_dict
  
imports: from __future__ import annotations
  import unittest
  from transformers import AlbertConfig, is_tf_available
  from transformers.models.auto import get_values
  from transformers.testing_utils import require_tf, slow
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
  from ...test_pipeline_mixin import PipelineTesterMixin
label_desc: Use image node_id 1 for calling the TFAlbertModelTest obj's underlying member method code with example usage: obj._prepare_for_class(inputs_dict, model_class, return_labels) and returns: inputs_dict