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",")"]
docstr: compute output shape
docstr_tok: ["compute","output","shape"]
code: def compute_output_shape(self, input_shape):
      """compute output shape"""
      return input_shape[0], input_shape[-1]
  
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 5 for calling the HanAttention obj's underlying member method code with example usage: obj.compute_output_shape(input_shape) and returns: input_shape,  input_shape