code: def get_multi_head_attention_model_inputs(input_dims):
      """Creates MultiHeadAttention symbolic model input."""
      (
          query_input_dims,
          value_input_dims,
          key_input_dims,
          mask_input_dims,
          use_key,
          use_mask,
      ) = input_dims
      query_input = tf.keras.Input(shape=query_input_dims)
      value_input = tf.keras.Input(shape=value_input_dims)
      key_input = tf.keras.Input(shape=key_input_dims)
      mask_input = tf.keras.Input(shape=mask_input_dims)
  
      input_args = (query_input, value_input)
      input_kwargs = {}
      if use_key:
          input_kwargs["key"] = key_input
      if use_mask:
          input_kwargs["attention_mask"] = mask_input
      return (
          input_args + (key_input, mask_input),
          input_args,
          input_kwargs,
      )
  
imports: from absl.testing import parameterized
  import tensorflow
  from tensorflow_privacy.privacy.fast_gradient_clipping import common_test_utils
  from tensorflow_privacy.privacy.fast_gradient_clipping import layer_registry
  from tensorflow_privacy.privacy.fast_gradient_clipping.registry_functions import dense
  from tensorflow_privacy.privacy.fast_gradient_clipping.registry_functions import multi_head_attention
label_desc: Use image node_id 8 for calling a global function with example usage: get_multi_head_attention_model_inputs(input_dims) and returns: unknown,  input_args,  input_kwargs