class_docstr: ernie encoder stack 
class_docstr_tok: ["ernie","encoder","stack"]
docstr: forward function 
docstr_tok: ["forward","function"]
code: def forward(
      self, inputs, attn_bias=None, past_cache=None, key_tag=None
  ):
      """forward function"""
      if past_cache is not None:
          assert isinstance(past_cache, tuple), (
              "unknown type of `past_cache`, expect tuple or list. got %s"
              % repr(type(past_cache))
          )
          past_cache = list(zip(*past_cache))
      else:
          past_cache = [None] * len(self.block)
      cache_list_k, cache_list_v, hidden_list = [], [], [inputs]
      checkpoint_name = []
  
      for b, p in zip(self.block, past_cache):
          inputs, cache = b(
              inputs, attn_bias=attn_bias, past_cache=p, key_tag=key_tag
          )
          cache_k, cache_v = cache
          cache_list_k.append(cache_k)
          cache_list_v.append(cache_v)
          hidden_list.append(inputs)
          checkpoint_name.append(inputs.name)
  
      return [
          inputs,
          hidden_list,
          (cache_list_k, cache_list_v),
          checkpoint_name,
      ]
  
imports: import logging
  import re
  import time
  import paddle
  from paddle import nn
  from paddle.nn import functional
label_desc: Use image node_id 2 for calling the ErnieEncoderStack obj's underlying member method code with example usage: obj.forward(inputs, attn_bias, past_cache, key_tag) and returns: list