class_docstr: Refer to [Hierarchical Attention Networks for Document Classification]
    (https://www.cs.cmu.edu/~hovy/papers/16HLT-hierarchical-attention-networks.pdf)
    wrap `with tf.variable_scope(name, reuse=tf.AUTO_REUSE):`
  Input shape: (Batch size, steps, features)
  Output shape: (Batch size, features)
class_docstr_tok: ["Refer","to","[","Hierarchical","Attention","Networks","for","Document","Classification","]","(","https",":","\/\/www.cs.cmu.edu\/~hovy\/papers\/16HLT-hierarchical-attention-networks.pdf",")","wrap","`","with","tf.variable_scope","(","name",",","reuse=tf.AUTO_REUSE",")",":","`","Input","shape",":","(","Batch","size",",","steps",",","features",")","Output","shape",":","(","Batch","size",",","features",")"]
code: def compute_mask(
      self, inputs, mask=None
  ):  # pylint: disable=unused-argument
      # do not pass the mask to the next layers
      return None
  
file_path: delta/delta/layers/attention.py
imports: from absl import logging
  import delta.compat
  from delta.layers.base_layer import Layer
label_desc: Use image node_id 3 for calling the HanAttention obj's underlying member method code with example usage: obj.compute_mask(inputs, mask) and returns: None