code: def __init__(self, config, *inputs, **kwargs):
      super().__init__(config, *inputs, **kwargs)
      self.num_labels = config.num_labels
      self.score = tf.keras.layers.Dense(
          config.num_labels,
          kernel_initializer=get_initializer(config.initializer_range),
          name="score",
          use_bias=False,
      )
      self.transformer = TFGPT2MainLayer(config, name="transformer")
  
imports: 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, TFSharedEmbeddings, get_initializer, keras_serializable, unpack_inputs
  from ...tf_utils import shape_list, stable_softmax
  from ...utils import DUMMY_INPUTS, 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 TFGPT2ForSequenceClassification object from inherited base classes: TFGPT2PreTrainedModel, TFSequenceClassificationLoss with example: obj = TFGPT2ForSequenceClassification(config)