code: def __init__(self, config, **kwargs):
      super().__init__(**kwargs)
  
      self.config = config
      self.output_hidden_states = config.output_hidden_states
      self.output_attentions = config.output_attentions
      self.use_cache = config.use_cache
      self.return_dict = config.use_return_dict
  
      self.d_model_size = config.n_embd
      self.num_layers = config.n_layer
  
      self.pos_encoding = positional_encoding(
          config.n_positions, self.d_model_size
      )
  
      self.w = TFSharedEmbeddings(
          config.vocab_size,
          config.n_embd,
          initializer_range=config.initializer_range,
          name="w",
      )
  
      self.dropout = tf.keras.layers.Dropout(config.embd_pdrop)
      self.h = [
          TFEncoderLayer(
              config.n_embd,
              config.n_head,
              config.dff,
              config.resid_pdrop,
              config.layer_norm_epsilon,
              self.output_attentions,
              name=f"h_._{i}",
          )
          for i in range(config.n_layer)
      ]
      self.layernorm = tf.keras.layers.LayerNormalization(
          epsilon=config.layer_norm_epsilon, name="layernorm"
      )
  
imports: import warnings
  from typing import Optional, Tuple, Union
  import numpy
  import tensorflow
  from ...modeling_tf_outputs import TFBaseModelOutputWithPast, TFCausalLMOutputWithPast, TFSequenceClassifierOutput
  from ...modeling_tf_utils import TFCausalLanguageModelingLoss, TFModelInputType, TFPreTrainedModel, TFSequenceClassificationLoss, TFSharedEmbeddings, get_initializer, keras_serializable, unpack_inputs
  from ...tf_utils import shape_list, stable_softmax
  from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
  from .configuration_ctrl import CTRLConfig
label_desc: Use image node_id 1 to create a new TFCTRLMainLayer object from inherited base classes: tf.keras.layers with example: obj = TFCTRLMainLayer(config)