code: def __init__(self, nx, config, scale=False, **kwargs):
      super().__init__(**kwargs)
  
      n_state = nx  # in Attention: n_state=768 (nx=n_embd)
      # [switch nx => n_state from Block to Attention to keep identical to TF implementation]
      assert (
          n_state % config.n_head == 0
      ), f"Hidden dimension {n_state} not dividable by number of heads {config.n_head}"
      self.n_head = config.n_head
      self.split_size = n_state
      self.scale = scale
      self.output_attentions = config.output_attentions
  
      self.c_attn = TFConv1D(
          n_state * 3,
          nx,
          initializer_range=config.initializer_range,
          name="c_attn",
      )
      self.c_proj = TFConv1D(
          n_state,
          nx,
          initializer_range=config.initializer_range,
          name="c_proj",
      )
      self.attn_dropout = tf.keras.layers.Dropout(config.attn_pdrop)
      self.resid_dropout = tf.keras.layers.Dropout(config.resid_pdrop)
      self.pruned_heads = set()
  
imports: from dataclasses import dataclass
  from typing import Optional, Tuple, Union
  import numpy
  import tensorflow
  from ...activations_tf import get_tf_activation
  from ...modeling_tf_outputs import TFBaseModelOutput, TFCausalLMOutput, TFSequenceClassifierOutput
  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 ModelOutput, add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
  from .configuration_openai import OpenAIGPTConfig
label_desc: Use image node_id 1 to create a new TFAttention object from inherited base classes: tf.keras.layers with example: obj = TFAttention(nx, config, scale)