code: def _attn(
      self,
      q,
      k,
      v,
      attention_mask,
      head_mask,
      output_attentions,
      training=False,
  ):
      # q, k, v have shape [batch, heads, sequence, features]
      w = tf.matmul(q, k, transpose_b=True)
      if self.scale:
          dk = tf.cast(
              shape_list(k)[-1], dtype=w.dtype
          )  # scale attention_scores
          w = w / tf.math.sqrt(dk)
  
      # w has shape [batch, heads, dst_sequence, src_sequence], where information flows from src to dst.
      _, _, nd, ns = shape_list(w)
      b = tf.cast(self.causal_attention_mask(nd, ns), dtype=w.dtype)
      b = tf.reshape(b, [1, 1, nd, ns])
      w = w * b - 1e4 * (1 - b)
  
      if attention_mask is not None:
          # Apply the attention mask
          attention_mask = tf.cast(attention_mask, dtype=w.dtype)
          w = w + attention_mask
  
      w = stable_softmax(w, axis=-1)
      w = self.attn_dropout(w, training=training)
  
      # Mask heads if we want to
      if head_mask is not None:
          w = w * head_mask
  
      outputs = [tf.matmul(w, v)]
      if output_attentions:
          outputs.append(w)
      return outputs
  
imports: from __future__ import annotations
  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 check_embeddings_within_bounds, 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 4 for calling the TFAttention obj's underlying member method code with example usage: obj._attn(q, k, v, attention_mask, head_mask, output_attentions, training) and returns: outputs