text: # Copyright (C) 2022. Huawei Technologies Co., Ltd. All rights reserved.
  
  # Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"),
  # to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense,
  # and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
  
  # The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
  
  # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
  # WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
  # COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
  # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
  
  
  import tensorflow as tf
  
  tf.random.set_seed(42)
  
  query = tf.random.uniform(shape=[2, 3, 3])
  value = tf.random.uniform(shape=[2, 2, 3])
  key = tf.random.uniform(shape=[2, 2, 3])
  attn = tf.keras.layers.AdditiveAttention(False)
  print("Attention: ", attn)
  # Calculate attention and gradients
  # Query
  with tf.GradientTape() as g:
      g.watch(query)
      y = attn([query, value, key])
  print("Query gradient", g.gradient(y, query))
  # Value
  with tf.GradientTape() as g:
      g.watch(value)
      y = attn([query, value, key])
  print("Value gradient", g.gradient(y, value))
  # Key
  with tf.GradientTape() as g:
      g.watch(key)
      y = attn([query, value, key])
  print("Key gradient", g.gradient(y, key))
  