code: def __init__(self, config: GPTJConfig, **kwargs):
      super().__init__(**kwargs)
  
      self.embed_dim = config.hidden_size
      self.num_attention_heads = config.num_attention_heads
      self.head_dim = self.embed_dim // self.num_attention_heads
      if self.head_dim * self.num_attention_heads != self.embed_dim:
          raise ValueError(
              f"embed_dim must be divisible by num_attention_heads (got `embed_dim`: {self.embed_dim} and"
              f" `num_attention_heads`: {self.num_attention_heads})."
          )
      self.scale_attn = self.head_dim**0.5
      self.rotary_dim = config.rotary_dim
  
      self.attn_dropout = tf.keras.layers.Dropout(config.attn_pdrop)
      self.resid_dropout = tf.keras.layers.Dropout(config.resid_pdrop)
  
      self.q_proj = tf.keras.layers.Dense(
          self.embed_dim,
          use_bias=False,
          kernel_initializer=get_initializer(config.initializer_range),
          name="q_proj",
      )
      self.k_proj = tf.keras.layers.Dense(
          self.embed_dim,
          use_bias=False,
          kernel_initializer=get_initializer(config.initializer_range),
          name="k_proj",
      )
      self.v_proj = tf.keras.layers.Dense(
          self.embed_dim,
          use_bias=False,
          kernel_initializer=get_initializer(config.initializer_range),
          name="v_proj",
      )
      self.out_proj = tf.keras.layers.Dense(
          self.embed_dim,
          use_bias=False,
          kernel_initializer=get_initializer(config.initializer_range),
          name="out_proj",
      )
  
      self.max_positions = config.max_position_embeddings
      self.lower_triangle_mask = tf.reshape(
          tf.cast(
              tf.experimental.numpy.tril(
                  tf.ones((self.max_positions, self.max_positions))
              ),
              tf.int8,
          ),
          (1, 1, self.max_positions, self.max_positions),
      )
      pos_embd_dim = self.rotary_dim or self.embed_dim
      self.embed_positions = create_sinusoidal_positions(
          self.max_positions, pos_embd_dim
      )
  
imports: from typing import Optional, Tuple, Union
  import numpy
  import tensorflow
  from ...activations_tf import get_tf_activation
  from ...file_utils import DUMMY_INPUTS, add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
  from ...modeling_tf_outputs import TFBaseModelOutputWithPast, TFCausalLMOutputWithPast, TFQuestionAnsweringModelOutput, TFSequenceClassifierOutputWithPast
  from ...modeling_tf_utils import TFCausalLanguageModelingLoss, TFModelInputType, TFPreTrainedModel, TFQuestionAnsweringLoss, TFSequenceClassificationLoss, TFSharedEmbeddings, get_initializer, keras_serializable, unpack_inputs
  from ...tf_utils import shape_list, stable_softmax
  from ...utils import logging
  from .configuration_gptj import GPTJConfig
label_desc: Use image node_id 1 to create a new TFGPTJAttention object from inherited base classes: tf.keras.layers with example: obj = TFGPTJAttention(config)