code: def __init__(self, config, *inputs, **kwargs):
      super().__init__(*inputs, **kwargs)
  
      self.config = config
      self.output_attentions = config.output_attentions
      self.output_hidden_states = config.output_hidden_states
      self.use_cache = config.use_cache
      self.return_dict = config.use_return_dict
  
      self.num_hidden_layers = config.n_layer
      self.vocab_size = config.vocab_size
      self.n_embd = config.n_embd
      self.n_positions = config.n_positions
      self.initializer_range = config.initializer_range
  
      self.wte = TFSharedEmbeddings(
          config.vocab_size,
          config.hidden_size,
          initializer_range=config.initializer_range,
          name="wte",
      )
      self.drop = tf.keras.layers.Dropout(config.embd_pdrop)
      self.h = [
          TFBlock(config, scale=True, name=f"h_._{i}")
          for i in range(config.n_layer)
      ]
      self.ln_f = tf.keras.layers.LayerNormalization(
          epsilon=config.layer_norm_epsilon, name="ln_f"
      )
  
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 TFGPT2MainLayer object from inherited base classes: tf.keras.layers with example: obj = TFGPT2MainLayer(config)