code: def call(
      self,
      hidden_states: tf.Tensor,
      layer_past: Optional[Tuple[tf.Tensor, tf.Tensor]] = None,
      attention_mask: tf.Tensor | None = None,
      position_ids: tf.Tensor | None = None,
      head_mask: tf.Tensor | None = None,
      use_cache: bool = False,
      output_attentions: bool = False,
  ):
      query = self.q_proj(hidden_states)
      key = self.k_proj(hidden_states)
      value = self.v_proj(hidden_states)
  
      query = self._split_heads(query, True)
      key = self._split_heads(key, True)
      value = self._split_heads(value, False)
  
      sincos = tf.cast(
          tf.gather(self.embed_positions, position_ids, axis=0),
          hidden_states.dtype,
      )
      sincos = tf.split(sincos, 2, axis=-1)
      if self.rotary_dim is not None:
          k_rot = key[:, :, :, : self.rotary_dim]
          k_pass = key[:, :, :, self.rotary_dim :]
  
          q_rot = query[:, :, :, : self.rotary_dim]
          q_pass = query[:, :, :, self.rotary_dim :]
  
          k_rot = apply_rotary_pos_emb(k_rot, sincos)
          q_rot = apply_rotary_pos_emb(q_rot, sincos)
  
          key = tf.concat((k_rot, k_pass), axis=-1)
          query = tf.concat((q_rot, q_pass), axis=-1)
      else:
          key = apply_rotary_pos_emb(key, sincos)
          query = apply_rotary_pos_emb(query, sincos)
  
      key = tf.transpose(key, (0, 2, 1, 3))
      query = tf.transpose(query, (0, 2, 1, 3))
  
      if layer_past is not None:
          past_key = layer_past[0]
          past_value = layer_past[1]
          key = tf.concat((past_key, key), axis=-2)
          value = tf.concat((past_value, value), axis=-2)
  
      if use_cache is True:
          present = (key, value)
      else:
          present = None
  
      # compute self-attention: V x Softmax(QK^T)
      attn_output, attn_weights = self._attn(
          query, key, value, attention_mask, head_mask
      )
  
      attn_output = self._merge_heads(attn_output)
      attn_output = self.out_proj(attn_output)
      attn_output = self.resid_dropout(attn_output)
  
      outputs = (attn_output, present)
      if output_attentions:
          outputs += (attn_weights,)
  
      return outputs
  
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 7 for calling the TFGPTJAttention obj's underlying member method code with example usage: obj.call(hidden_states, layer_past, attention_mask, position_ids, head_mask, use_cache, output_attentions) and returns: outputs