class_docstr: Definitely can represent BERT, but also a lot of other things. To be used for MLM schemes.
class_docstr_tok: ["Definitely","can","represent","BERT",",","but","also","a","lot","of","other","things",".","To","be","used","for","MLM","schemes","."]
docstr: Take another step forward on a set of given hidden states.
docstr_tok: ["Take","another","step","forward","on","a","set","of","given","hidden","states","."]
code: def forward_step(
      self, hidden_states, attention_mask: Optional[torch.Tensor] = None
  ):
      """Take another step forward on a set of given hidden states."""
      hidden_states = self.recurrent_layer(
          hidden_states, attention_mask
      )
      return hidden_states
  
imports: import torch
  from typing import Optional
  from random import randrange
  from transformers import PretrainedConfig
  from .components import EmbeddingComponent, AttentionComponent, FFNComponent, PredictionHeadComponent, get_extended_attention_mask, _get_norm_fn, _get_nonlin_fn, _init_module
label_desc: Use image node_id 5 for calling the ScriptableRecurrentLM obj's underlying member method code with example usage: obj.forward_step(hidden_states, attention_mask) and returns: hidden_states