code: def __init__(self, config, *inputs, **kwargs):
      super().__init__(config, *inputs, **kwargs)
      config.num_labels = 1
      self.transformer = TFGPT2MainLayer(config, name="transformer")
      self.multiple_choice_head = TFSequenceSummary(
          config,
          initializer_range=config.initializer_range,
          name="multiple_choice_head",
      )
  
imports: from __future__ import annotations
  from dataclasses import dataclass
  from typing import List, Optional, Tuple, Union
  import numpy
  import tensorflow
  from ...activations_tf import get_tf_activation
  from ...modeling_tf_outputs import TFBaseModelOutputWithPastAndCrossAttentions, TFCausalLMOutputWithCrossAttentions, TFSequenceClassifierOutputWithPast
  from ...modeling_tf_utils import TFCausalLanguageModelingLoss, TFConv1D, TFModelInputType, TFPreTrainedModel, TFSequenceClassificationLoss, TFSequenceSummary, get_initializer, keras_serializable, unpack_inputs
  from ...tf_utils import check_embeddings_within_bounds, shape_list, stable_softmax
  from ...utils import ModelOutput, add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
  from .configuration_gpt2 import GPT2Config
label_desc: Use image node_id 1 to create a new TFGPT2DoubleHeadsModel object from inherited base classes: TFGPT2PreTrainedModel with example: obj = TFGPT2DoubleHeadsModel(config)