code: def get_dilated_1d_attention_mask(
      num_heads,
      block_size,
      num_blocks,
      memory_size,
      gap_size,
      name="dilated_mask",
  ):
      """Dilated attention with a masking strategy."""
      mask = np.ones((num_heads, block_size, 2 * block_size), bool)
  
      # now going over every row to do the right assignment of
      # memory blocks
      for i in range(block_size):
          visible = 2 * block_size - (block_size - i)
          # You always attend to yourself, set the mask for that
          mask[:, i, -(block_size - i)] = 0
          # Maybe num_blocks can be automatically calculated?
          for j in range(num_blocks):
              for k in range(memory_size):
                  index = ((gap_size + memory_size) * j) + k
                  if index >= visible:
                      break
                  mask[
                      :, i, -(index + block_size - i + 1)
                  ] = 0  # Verify
  
      # adding a num blocks dimension
      mask = np.expand_dims(mask, axis=1)
      return tf.constant(mask, dtype=tf.int32, name=name)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import numpy
  from six.moves import range
  from tensor2tensor.layers import common_attention
  from tensor2tensor.layers import common_layers
  from tensor2tensor.utils import expert_utils
  import tensorflow.compat.v1
  from tensorflow.compat.v1 import estimator
label_desc: Use image node_id 5 for calling a global function with example usage: get_dilated_1d_attention_mask(num_heads, block_size, num_blocks, memory_size, gap_size, name) and returns: tf