code: def build(self, input_shape=None):
      if self.built:
          return
      self.built = True
      if getattr(self, "q_proj", None) is not None:
          with tf.name_scope(self.q_proj.name):
              self.q_proj.build([None, None, self.embed_dim])
      if getattr(self, "k_proj", None) is not None:
          with tf.name_scope(self.k_proj.name):
              self.k_proj.build([None, None, self.embed_dim])
      if getattr(self, "v_proj", None) is not None:
          with tf.name_scope(self.v_proj.name):
              self.v_proj.build([None, None, self.embed_dim])
      if getattr(self, "out_proj", None) is not None:
          with tf.name_scope(self.out_proj.name):
              self.out_proj.build([None, None, self.embed_dim])
  
imports: from __future__ import annotations
  from typing import Optional, Tuple, Union
  import numpy
  import tensorflow
  from ...activations_tf import get_tf_activation
  from ...file_utils import 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 check_embeddings_within_bounds, shape_list, stable_softmax
  from ...utils import logging
  from .configuration_gptj import GPTJConfig
label_desc: Use image node_id 8 for calling the TFGPTJAttention obj's underlying member method code with example usage: obj.build(input_shape) without return types