code: def _attn(
      self,
      query: tf.Tensor,
      key: tf.Tensor,
      value: tf.Tensor,
      attention_mask: Optional[tf.Tensor] = None,
      head_mask: Optional[tf.Tensor] = None,
  ) -> Tuple[tf.Tensor, tf.Tensor]:
      # compute causal mask from causal mask buffer
      query_length, key_length = (
          shape_list(query)[-2],
          shape_list(key)[-2],
      )
      causal_mask = self.get_causal_mask(key_length, query_length)
  
      # Keep the attention weights computation in fp32 to avoid overflow issues
      query = tf.cast(query, tf.float32)
      key = tf.cast(key, tf.float32)
  
      attn_weights = tf.matmul(query, key, transpose_b=True)
      attn_weights = tf.where(
          causal_mask,
          attn_weights,
          self.get_masked_bias(attn_weights.dtype),
      )
  
      attn_weights = attn_weights / self.scale_attn
  
      if attention_mask is not None:
          # Apply the attention mask
          attn_weights = attn_weights + attention_mask
  
      attn_weights = stable_softmax(attn_weights, axis=-1)
      attn_weights = tf.cast(attn_weights, value.dtype)
      attn_weights = self.attn_dropout(attn_weights)
  
      # Mask heads if we want to
      if head_mask is not None:
          attn_weights = attn_weights * head_mask
  
      attn_output = tf.matmul(attn_weights, value)
  
      return attn_output, attn_weights
  
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 6 for calling the TFGPTJAttention obj's underlying member method code with example usage: obj._attn(query, key, value, attention_mask, head_mask) and returns: attn_output,  attn_weights