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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def attention_image_summary(attn, image_shapes=None):
 """Compute color image summary. Args: attn: a Tensor with shape [batch, num_heads, query_length, memory_length] image_shapes: optional tuple of integer scalars. If the query positions and memory positions represent the pixels of flattened images, then pass in their dimensions: (query_rows, query_cols, memory_rows, memory_cols). If the query positions and memory positions represent the pixels x channels of flattened images, then pass in their dimensions: (query_rows, query_cols, query_channels, memory_rows, memory_cols, memory_channels). """

  attn = tf.cast(attn, tf.float32)
  num_heads = common_layers.shape_list(attn)[1]
  # [batch, query_length, memory_length, num_heads]
  image = tf.transpose(attn, [0, 2, 3, 1])
  image = tf.pow(image, 0.2)  # for high-dynamic-range
  # Each head will correspond to one of RGB.
  # pad the heads to be a multiple of 3
  image = tf.pad(image, [[0, 0], [0, 0], [0, 0], [0, tf.mod(-num_heads, 3)]])
  image = split_last_dimension(image, 3)
  image = tf.reduce_max(image, 4)
  if image_shapes is not None:
    if len(image_shapes) == 4:
      q_rows, q_cols, m_rows, m_cols = list(image_shapes)
      image = tf.reshape(image, [-1, q_rows, q_cols, m_rows, m_cols, 3])
      image = tf.transpose(image, [0, 1, 3, 2, 4, 5])
      image = tf.reshape(image, [-1, q_rows * m_rows, q_cols * m_cols, 3])
    else:
      assert len(image_shapes) == 6
      q_rows, q_cols, q_channnels, m_rows, m_cols, m_channels = list(
          image_shapes)
      image = tf.reshape(
          image,
          [-1, q_rows, q_cols, q_channnels, m_rows, m_cols, m_channels, 3])
      image = tf.transpose(image, [0, 1, 4, 3, 2, 5, 6, 7])
      image = tf.reshape(
          image,
          [-1, q_rows * m_rows * q_channnels, q_cols * m_cols * m_channels, 3])
  tf.summary.image("attention", image, max_outputs=1)